Telecom Users Churn Machine Learning model¶

This notebook looks into using Python-based Machine-learning and Data Science libraries in an attempt to build a machine learning model capable of identifying whether or not people will renew their contract.

Any business wants to maximize the number of customers. To achieve this goal, it is important not only to try to attract new ones, but also to retain existing ones. Retaining a client will cost the company less than attracting a new one. In addition, a new client may be weakly interested in business services and it will be difficult to work with him, while old clients already have the necessary data on interaction with the service.

Accordingly, predicting the churn, we can react in time and try to keep the client who wants to leave. Based on the data about the services that the client uses, we can make him a special offer, trying to change his decision to leave the operator. This will make the task of retention easier to implement than the task of attracting new users, about which we do not know anything yet.

1. Problem Definition¶

Statement :

The task is to analyze the data and predict the churn of users (to identify people who will and will not renew their contract).

Churn :¶

Churn is a measurement of the percentage of accounts that cancel or choose not to renew their subscriptions. Churn is the measure of how many customers stop using a product. This can be measured based on actual usage or failure to renew (when the product is sold using a subscription model).

2. Data¶

The data we are using comes from Kaggle:
https://www.kaggle.com/radmirzosimov/telecom-users-dataset

3. Evaluation¶

If we can reach maximum accuracy at predicting whether or not a patient will renew his subscrption.

4. Features¶

1. customerID - customer id
2. gender - client gender (male / female)
3. SeniorCitizen - is the client retired (1, 0)
4. Partner - is the client married (Yes, No)
5. tenure - how many months a person has been a client of the company
6. PhoneService - is the telephone service connected (Yes, No)
7. MultipleLines - are multiple phone lines connected (Yes, No, No phone service)
8. InternetService - client's Internet service provider (DSL, Fiber optic, No)
9. OnlineSecurity - is the online security service connected (Yes, No, No internet service)
10. OnlineBackup - is the online backup service activated (Yes, No, No internet service)
11. DeviceProtection - does the client have equipment insurance (Yes, No, No internet service)
12. TechSupport - is the technical support service connected (Yes, No, No internet service)
13. StreamingTV - is the streaming TV service connected (Yes, No, No internet service)
14. StreamingMovies - is the streaming cinema service activated (Yes, No, No internet service)
15. Contract - type of customer contract (Month-to-month, One year, Two year)
16. PaperlessBilling - whether the client uses paperless billing (Yes, No)
17. PaymentMethod - payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
18. MonthlyCharges - current monthly payment
19. TotalCharges - the total amount that the client paid for the services for the entire time
20. Churn - whether there was a churn (Yes or No)

The work should include the following ma*ndatory items:**

• Description of the data (with the calculation of basic statistics);
• Research of dependencies and formulation of hypotheses;
• Building models for predicting the outflow (with justification for the choice of a particular model) based on tested hypotheses and identified relationships
• Comparison of the quality of the obtained models.

In [19]:

# Importing the tools we need 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn

# Importing the tools we need import pandas as pd import numpy as np import matplotlib.pyplot as plt import sklearn

In [20]:

# Getting our data into our Jupyter notebook
df = pd.read_csv("data/telecom_users.csv")
df.head()

# Getting our data into our Jupyter notebook df = pd.read_csv("data/telecom_users.csv") df.head()

Out[20]:

	Unnamed: 0	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	1869	7010-BRBUU	Male	0	Yes	Yes	72	Yes	Yes	No	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.10	1734.65	No
1	4528	9688-YGXVR	Female	0	No	No	44	Yes	No	Fiber optic	...	Yes	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	88.15	3973.2	No
2	6344	9286-DOJGF	Female	1	Yes	No	38	Yes	Yes	Fiber optic	...	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	74.95	2869.85	Yes
3	6739	6994-KERXL	Male	0	No	No	4	Yes	No	DSL	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	55.90	238.5	No
4	432	2181-UAESM	Male	0	No	No	2	Yes	No	DSL	...	Yes	No	No	No	Month-to-month	No	Electronic check	53.45	119.5	No

5 rows × 22 columns

Looks like our dataframe has an unnamed column and we are not able to figure out what it is so, let's delete it and continue further.¶

In [21]:

# To delete the unnamed column 
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

# To delete the unnamed column df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

In [22]:

df.head()

df.head()

Out[22]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7010-BRBUU	Male	0	Yes	Yes	72	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.10	1734.65	No
1	9688-YGXVR	Female	0	No	No	44	Yes	No	Fiber optic	No	...	Yes	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	88.15	3973.2	No
2	9286-DOJGF	Female	1	Yes	No	38	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	74.95	2869.85	Yes
3	6994-KERXL	Male	0	No	No	4	Yes	No	DSL	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	55.90	238.5	No
4	2181-UAESM	Male	0	No	No	2	Yes	No	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Electronic check	53.45	119.5	No

5 rows × 21 columns

In [23]:

# To get the insights of our data we are working with
df.info()

# To get the insights of our data we are working with df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5986 entries, 0 to 5985
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        5986 non-null   object 
 1   gender            5986 non-null   object 
 2   SeniorCitizen     5986 non-null   int64  
 3   Partner           5986 non-null   object 
 4   Dependents        5986 non-null   object 
 5   tenure            5986 non-null   int64  
 6   PhoneService      5986 non-null   object 
 7   MultipleLines     5986 non-null   object 
 8   InternetService   5986 non-null   object 
 9   OnlineSecurity    5986 non-null   object 
 10  OnlineBackup      5986 non-null   object 
 11  DeviceProtection  5986 non-null   object 
 12  TechSupport       5986 non-null   object 
 13  StreamingTV       5986 non-null   object 
 14  StreamingMovies   5986 non-null   object 
 15  Contract          5986 non-null   object 
 16  PaperlessBilling  5986 non-null   object 
 17  PaymentMethod     5986 non-null   object 
 18  MonthlyCharges    5986 non-null   float64
 19  TotalCharges      5986 non-null   object 
 20  Churn             5986 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 982.2+ KB

In [24]:

# To check for any empty entries in our data
df.isna().sum()

# To check for any empty entries in our data df.isna().sum()

Out[24]:

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

In [25]:

df.columns

df.columns

Out[25]:

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

In [26]:

df.MonthlyCharges.plot.hist(color="orange")

df.MonthlyCharges.plot.hist(color="orange")

Out[26]:

<matplotlib.axes._subplots.AxesSubplot at 0x26afe5dd308>

In [27]:

df["MonthlyCharges"].median()

df["MonthlyCharges"].median()

Out[27]:

70.4

In [28]:

df.head()

df.head()

Out[28]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7010-BRBUU	Male	0	Yes	Yes	72	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.10	1734.65	No
1	9688-YGXVR	Female	0	No	No	44	Yes	No	Fiber optic	No	...	Yes	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	88.15	3973.2	No
2	9286-DOJGF	Female	1	Yes	No	38	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	74.95	2869.85	Yes
3	6994-KERXL	Male	0	No	No	4	Yes	No	DSL	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	55.90	238.5	No
4	2181-UAESM	Male	0	No	No	2	Yes	No	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Electronic check	53.45	119.5	No

5 rows × 21 columns

In [29]:

# Determining no. Senior citizens
df["SeniorCitizen"].value_counts()

# Determining no. Senior citizens df["SeniorCitizen"].value_counts()

Out[29]:

0    5020
1     966
Name: SeniorCitizen, dtype: int64

In [30]:

# Finding no. of rows
len(df)

# Finding no. of rows len(df)

Out[30]:

5986

In [31]:

# Check for Male-Female
df["gender"].value_counts()

# Check for Male-Female df["gender"].value_counts()

Out[31]:

Male      3050
Female    2936
Name: gender, dtype: int64

Most of our data labels are in string format to get them used we need to convert them into categories¶

In [32]:

# Check for dtype-object (strings)
pd.api.types.is_string_dtype(df["Partner"])

# Check for dtype-object (strings) pd.api.types.is_string_dtype(df["Partner"])

Out[32]:

True

In [33]:

# Finding the columns which contain strings (dtype-object) 
for label, content in df.items():
    if pd.api.types.is_string_dtype(content):
        print(label)

# Finding the columns which contain strings (dtype-object) for label, content in df.items(): if pd.api.types.is_string_dtype(content): print(label)

customerID
gender
Partner
Dependents
PhoneService
MultipleLines
InternetService
OnlineSecurity
OnlineBackup
DeviceProtection
TechSupport
StreamingTV
StreamingMovies
Contract
PaperlessBilling
PaymentMethod
TotalCharges
Churn

In [34]:

# converting all of the string values into category values
for label, content in df.items():
    if pd.api.types.is_string_dtype(content):
        df[label]=content.astype("category").cat.as_ordered()

# converting all of the string values into category values for label, content in df.items(): if pd.api.types.is_string_dtype(content): df[label]=content.astype("category").cat.as_ordered()

In [35]:

df.info()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5986 entries, 0 to 5985
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype   
---  ------            --------------  -----   
 0   customerID        5986 non-null   category
 1   gender            5986 non-null   category
 2   SeniorCitizen     5986 non-null   int64   
 3   Partner           5986 non-null   category
 4   Dependents        5986 non-null   category
 5   tenure            5986 non-null   int64   
 6   PhoneService      5986 non-null   category
 7   MultipleLines     5986 non-null   category
 8   InternetService   5986 non-null   category
 9   OnlineSecurity    5986 non-null   category
 10  OnlineBackup      5986 non-null   category
 11  DeviceProtection  5986 non-null   category
 12  TechSupport       5986 non-null   category
 13  StreamingTV       5986 non-null   category
 14  StreamingMovies   5986 non-null   category
 15  Contract          5986 non-null   category
 16  PaperlessBilling  5986 non-null   category
 17  PaymentMethod     5986 non-null   category
 18  MonthlyCharges    5986 non-null   float64 
 19  TotalCharges      5986 non-null   category
 20  Churn             5986 non-null   category
dtypes: category(18), float64(1), int64(2)
memory usage: 608.1 KB

In [36]:

df.Partner.cat.categories

df.Partner.cat.categories

Out[36]:

Index(['No', 'Yes'], dtype='object')

In [37]:

df.Partner.cat.codes

df.Partner.cat.codes

Out[37]:

0       1
1       0
2       1
3       0
4       0
       ..
5981    1
5982    1
5983    1
5984    0
5985    0
Length: 5986, dtype: int8

Now, we' ve access to all of our data in form of numbers¶

In [38]:

# Our data is ready, saving this preprocessed data into a new file
df.to_csv("data/train.csv",
              index=False)

# Our data is ready, saving this preprocessed data into a new file df.to_csv("data/train.csv", index=False)

In [39]:

# Importing preprocessed data
df = pd.read_csv("data/train.csv",
                 low_memory=False)

df.head()

# Importing preprocessed data df = pd.read_csv("data/train.csv", low_memory=False) df.head()

Out[39]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7010-BRBUU	Male	0	Yes	Yes	72	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.10	1734.65	No
1	9688-YGXVR	Female	0	No	No	44	Yes	No	Fiber optic	No	...	Yes	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	88.15	3973.2	No
2	9286-DOJGF	Female	1	Yes	No	38	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	74.95	2869.85	Yes
3	6994-KERXL	Male	0	No	No	4	Yes	No	DSL	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	55.90	238.5	No
4	2181-UAESM	Male	0	No	No	2	Yes	No	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Electronic check	53.45	119.5	No

5 rows × 21 columns

In [40]:

# Recheck for missing values
df.isna().sum()

# Recheck for missing values df.isna().sum()

Out[40]:

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

Filling and turning categorical variables into numbers

In [41]:

# Check for columns which aren't numeric
for label, content in df.items():
    if not pd.api.types.is_numeric_dtype(content):
        print(label)

# Check for columns which aren't numeric for label, content in df.items(): if not pd.api.types.is_numeric_dtype(content): print(label)

customerID
gender
Partner
Dependents
PhoneService
MultipleLines
InternetService
OnlineSecurity
OnlineBackup
DeviceProtection
TechSupport
StreamingTV
StreamingMovies
Contract
PaperlessBilling
PaymentMethod
TotalCharges
Churn

In [42]:

# Turning categorical numbers into numbers and fill missing
for label, content in df.items():
    if not pd.api.types.is_numeric_dtype(content):
        # Turn categories into numbers and add +1
        df[label] = pd.Categorical(content).codes+1
        
df.info()

# Turning categorical numbers into numbers and fill missing for label, content in df.items(): if not pd.api.types.is_numeric_dtype(content): # Turn categories into numbers and add +1 df[label] = pd.Categorical(content).codes+1 df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5986 entries, 0 to 5985
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        5986 non-null   int16  
 1   gender            5986 non-null   int8   
 2   SeniorCitizen     5986 non-null   int64  
 3   Partner           5986 non-null   int8   
 4   Dependents        5986 non-null   int8   
 5   tenure            5986 non-null   int64  
 6   PhoneService      5986 non-null   int8   
 7   MultipleLines     5986 non-null   int8   
 8   InternetService   5986 non-null   int8   
 9   OnlineSecurity    5986 non-null   int8   
 10  OnlineBackup      5986 non-null   int8   
 11  DeviceProtection  5986 non-null   int8   
 12  TechSupport       5986 non-null   int8   
 13  StreamingTV       5986 non-null   int8   
 14  StreamingMovies   5986 non-null   int8   
 15  Contract          5986 non-null   int8   
 16  PaperlessBilling  5986 non-null   int8   
 17  PaymentMethod     5986 non-null   int8   
 18  MonthlyCharges    5986 non-null   float64
 19  TotalCharges      5986 non-null   int16  
 20  Churn             5986 non-null   int8   
dtypes: float64(1), int16(2), int64(2), int8(16)
memory usage: 257.3 KB

In [43]:

df.head().T

df.head().T

Out[43]:

	0	1	2	3	4
customerID	4253.0	5807.00	5578.00	4244.0	1271.00
gender	2.0	1.00	1.00	2.0	2.00
SeniorCitizen	0.0	0.00	1.00	0.0	0.00
Partner	2.0	1.00	2.00	1.0	1.00
Dependents	2.0	1.00	1.00	1.0	1.00
tenure	72.0	44.00	38.00	4.0	2.00
PhoneService	2.0	2.00	2.00	2.0	2.00
MultipleLines	3.0	1.00	3.00	1.0	1.00
InternetService	3.0	2.00	2.00	1.0	1.00
OnlineSecurity	2.0	1.00	1.00	1.0	3.00
OnlineBackup	2.0	3.00	1.00	1.0	1.00
DeviceProtection	2.0	3.00	1.00	1.0	3.00
TechSupport	2.0	1.00	1.00	1.0	1.00
StreamingTV	2.0	3.00	1.00	1.0	1.00
StreamingMovies	2.0	1.00	1.00	3.0	1.00
Contract	3.0	1.00	1.00	1.0	1.00
PaperlessBilling	1.0	2.00	2.00	2.0	1.00
PaymentMethod	2.0	2.00	1.00	3.0	3.00
MonthlyCharges	24.1	88.15	74.95	55.9	53.45
TotalCharges	1067.0	2902.00	2127.00	1734.0	309.00
Churn	1.0	1.00	2.00	1.0	1.00

Now our data is numeric and has no missing values so we can start building our Machine Learning Model

In [44]:

# ML models and some extra tools
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import precision_score, recall_score, f1_score 
from sklearn.metrics import plot_roc_curve

# ML models and some extra tools from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split, cross_val_score from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.metrics import confusion_matrix, classification_report from sklearn.metrics import precision_score, recall_score, f1_score from sklearn.metrics import plot_roc_curve

In [45]:

%%time
# Instatiate model 
model = RandomForestClassifier(n_jobs=-1,
                              random_state=42)

# Fitting the model 
model.fit(df.drop("Churn", axis=1), df["Churn"])

%%time # Instatiate model model = RandomForestClassifier(n_jobs=-1, random_state=42) # Fitting the model model.fit(df.drop("Churn", axis=1), df["Churn"])

Wall time: 289 ms

Out[45]:

RandomForestClassifier(n_jobs=-1, random_state=42)

In [46]:

# Scoring the model
model.score(df.drop("Churn", axis=1), df["Churn"]) * 100

# Scoring the model model.score(df.drop("Churn", axis=1), df["Churn"]) * 100

Out[46]:

100.0

In [47]:

df.head(20)

df.head(20)

Out[47]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	4253	2	0	2	2	72	2	3	3	2	...	2	2	2	2	3	1	2	24.10	1067	1
1	5807	1	0	1	1	44	2	1	2	1	...	3	1	3	1	1	2	2	88.15	2902	1
2	5578	1	1	2	1	38	2	3	2	1	...	1	1	1	1	1	2	1	74.95	2127	2
3	4244	2	0	1	1	4	2	1	1	1	...	1	1	1	3	1	2	3	55.90	1734	1
4	1271	2	0	1	1	2	2	1	1	3	...	3	1	1	1	1	1	3	53.45	309	1
5	2560	1	0	2	1	70	1	2	1	3	...	3	3	1	3	3	2	1	49.85	2508	1
6	1473	1	0	1	1	33	2	3	2	3	...	1	1	1	3	1	2	3	90.65	2220	1
7	2592	1	0	1	1	1	1	2	1	1	...	1	1	1	1	1	2	4	24.90	1757	1
8	5364	2	0	1	1	39	1	2	1	1	...	3	3	1	1	2	1	4	35.55	490	1
9	4842	2	1	1	1	55	2	3	2	3	...	3	3	3	3	1	2	3	116.50	4420	1
10	2724	2	0	2	2	52	2	1	1	1	...	3	3	3	1	2	2	3	68.75	2590	1
11	1225	1	0	2	2	30	1	2	1	1	...	1	3	3	3	1	1	2	51.20	859	2
12	1353	2	1	2	1	60	2	3	2	1	...	1	3	3	3	1	2	3	99.00	4230	1
13	1484	2	0	2	2	50	2	3	1	1	...	3	1	1	1	2	1	1	54.90	1943	1
14	1618	1	0	1	1	32	2	3	2	3	...	3	3	3	3	2	2	1	109.55	2675	1
15	1044	2	0	2	1	51	2	3	2	1	...	3	1	3	3	2	2	1	106.80	3912	1
16	1457	2	0	1	1	1	2	1	2	1	...	1	3	1	1	1	2	4	74.30	4917	1
17	3069	1	0	2	2	69	2	3	3	2	...	2	2	2	2	3	2	1	25.60	996	1
18	5240	1	0	2	2	42	2	3	2	1	...	1	1	3	3	1	2	3	94.20	3066	2
19	350	2	0	2	1	14	1	2	1	1	...	1	1	3	3	1	1	3	46.35	4554	2

20 rows × 21 columns

In [48]:

df["Churn"].value_counts()

df["Churn"].value_counts()

Out[48]:

1    4399
2    1587
Name: Churn, dtype: int64

Modelling¶

In [49]:

# Splitting data into X and Y
X = df.drop("Churn", axis=1)

Y = df["Churn"]

# Splitting data into X and Y X = df.drop("Churn", axis=1) Y = df["Churn"]

In [50]:

X.head()

X.head()

Out[50]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges
0	4253	2	0	2	2	72	2	3	3	2	2	2	2	2	2	3	1	2	24.10	1067
1	5807	1	0	1	1	44	2	1	2	1	3	3	1	3	1	1	2	2	88.15	2902
2	5578	1	1	2	1	38	2	3	2	1	1	1	1	1	1	1	2	1	74.95	2127
3	4244	2	0	1	1	4	2	1	1	1	1	1	1	1	3	1	2	3	55.90	1734
4	1271	2	0	1	1	2	2	1	1	3	1	3	1	1	1	1	1	3	53.45	309

In [51]:

Y.head()

Y.head()

Out[51]:

0    1
1    1
2    2
3    1
4    1
Name: Churn, dtype: int8

In [52]:

# Splitting data into train and test sets
np.random.seed(42)

X_train, X_test, Y_train, Y_test = train_test_split(X,
                                                    Y,
                                                    test_size=0.2)

# Splitting data into train and test sets np.random.seed(42) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

In [53]:

X_train.head()

X_train.head()

Out[53]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges
5737	3350	1	1	2	1	28	2	3	3	2	2	2	2	2	2	1	1	4	25.70	4893
829	5554	1	0	1	1	6	1	2	1	1	1	3	1	3	3	1	2	3	47.95	2283
2694	754	2	0	1	1	55	2	3	2	1	1	1	1	3	3	1	2	3	96.80	3797
1496	5130	1	0	2	2	54	2	3	1	1	3	3	1	1	1	3	2	1	59.80	2424
438	5511	1	0	1	1	29	2	1	3	2	2	2	2	2	2	1	1	2	19.35	4227

In [54]:

Y_train.head()

Y_train.head()

Out[54]:

5737    1
829     2
2694    2
1496    1
438     1
Name: Churn, dtype: int8

We will be trying 3 different Machine Learning models:¶

1. Logistic Regression
2. K-Nearest Neighbours Classifier
3. Random Forest Classifier

In [55]:

# Put models in a dictionary
models = {"Logistic Regression": LogisticRegression(solver='liblinear'), 
          "KNN": KNeighborsClassifier(),
          "Random Forest": RandomForestClassifier()}

# Create a function to fit and score models
def fit_and_score(models, X_train, X_test, Y_train, Y_test):
    """
    Fits and evaluates given machine learning models.
    models : a dict of differetn Scikit-Learn machine learning models
    X_train : training data (no labels)
    X_test : testing data (no labels)
    Y_train : training labels
    Y_test : test labels
    """
    # Set random seed
    np.random.seed(42)
    # Make a dictionary to keep model scores
    model_scores = {}
    # Loop through models
    for name, model in models.items():
        # Fit the model to the data
        model.fit(X_train, Y_train)
        # Evaluate the model and append its score to model_scores
        model_scores[name] = model.score(X_test, Y_test)
    return model_scores

# Put models in a dictionary models = {"Logistic Regression": LogisticRegression(solver='liblinear'), "KNN": KNeighborsClassifier(), "Random Forest": RandomForestClassifier()} # Create a function to fit and score models def fit_and_score(models, X_train, X_test, Y_train, Y_test): """ Fits and evaluates given machine learning models. models : a dict of differetn Scikit-Learn machine learning models X_train : training data (no labels) X_test : testing data (no labels) Y_train : training labels Y_test : test labels """ # Set random seed np.random.seed(42) # Make a dictionary to keep model scores model_scores = {} # Loop through models for name, model in models.items(): # Fit the model to the data model.fit(X_train, Y_train) # Evaluate the model and append its score to model_scores model_scores[name] = model.score(X_test, Y_test) return model_scores

In [56]:

model_scores = fit_and_score(models=models,
                             X_train=X_train,
                             X_test=X_test,
                             Y_train=Y_train,
                             Y_test=Y_test)

model_scores

model_scores = fit_and_score(models=models, X_train=X_train, X_test=X_test, Y_train=Y_train, Y_test=Y_test) model_scores

Out[56]:

{'Logistic Regression': 0.8005008347245409,
 'KNN': 0.6869782971619366,
 'Random Forest': 0.7863105175292153}

Model Comparison¶

In [57]:

model_compare = pd.DataFrame(model_scores, index=["accuracy"])
model_compare.T.plot.bar(color="orange");

model_compare = pd.DataFrame(model_scores, index=["accuracy"]) model_compare.T.plot.bar(color="orange");

Improving and Analysing our models :

• Hyperparameter tuning
• Feature Importance
• Confusion Matrix
• Cross-Validation
• Precision
• Recall
• F1 score
• Classification Report
• Reciever Operating Characteristic curve (ROC)
• Area under the curve (AUC)

Tuning our models with Randomized Search CV :¶

• LogisticRegression()
• RandomForestClassifier()
• KNeighborsClassifier()

In [78]:

# Creating grid for LogisticRegression()
log_reg_grid = {"C":np.logspace(-4,4,20),
                "solver" : ["newton-cg",  "lbfgs", "liblinear", "sag", "saga"],
                "penalty" : ["none", "l1", "l2", "elasticnet"],
                "max_iter" : np.arange(1, 200, 5)}

# Creating grid for RandomForestClassifier()
rf_grid = {"n_estimators": np.arange(10,1000,50),
           "max_features": np.arange(1, 100, 10),
           "max_depth": [None, 3,5,10],
           "min_samples_split": np.arange(2,100, 4),
           "min_samples_leaf": np.arange(1,100, 5)}

# Creating grid for LogisticRegression() log_reg_grid = {"C":np.logspace(-4,4,20), "solver" : ["newton-cg", "lbfgs", "liblinear", "sag", "saga"], "penalty" : ["none", "l1", "l2", "elasticnet"], "max_iter" : np.arange(1, 200, 5)} # Creating grid for RandomForestClassifier() rf_grid = {"n_estimators": np.arange(10,1000,50), "max_features": np.arange(1, 100, 10), "max_depth": [None, 3,5,10], "min_samples_split": np.arange(2,100, 4), "min_samples_leaf": np.arange(1,100, 5)}

Tuning our models with RadomizedSearchCV()

In [85]:

%%time
# Tuning LogisticRegression
np.random.seed(42)

# Setting up random hyperparameter search for LogisticRegression
rs_log_reg = RandomizedSearchCV(LogisticRegression(),
                               param_distributions=log_reg_grid,
                               cv=5,
                               n_iter=500,
                               verbose=True)

# Fit random hyperparameter search model for LogisticRegression
rs_log_reg.fit(X_train, Y_train);

%%time # Tuning LogisticRegression np.random.seed(42) # Setting up random hyperparameter search for LogisticRegression rs_log_reg = RandomizedSearchCV(LogisticRegression(), param_distributions=log_reg_grid, cv=5, n_iter=500, verbose=True) # Fit random hyperparameter search model for LogisticRegression rs_log_reg.fit(X_train, Y_train);

Fitting 5 folds for each of 500 candidates, totalling 2500 fits

F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging
  warn(msg, LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:195: UserWarning: Line Search failed
  warnings.warn('Line Search failed')
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging
  warn(msg, LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:195: UserWarning: Line Search failed
  warnings.warn('Line Search failed')
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
    " got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging
  warn(msg, LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:195: UserWarning: Line Search failed
  warnings.warn('Line Search failed')
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging
  warn(msg, LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:195: UserWarning: Line Search failed
  warnings.warn('Line Search failed')
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging
  warn(msg, LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:195: UserWarning: Line Search failed
  warnings.warn('Line Search failed')
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\svm\_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
    " got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
  "number of iterations.", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  "the coef_ did not converge", ConvergenceWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\scipy\optimize\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge
  warn('The line search algorithm did not converge', LineSearchWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
  "Setting penalty='none' will ignore the C and l1_ratio "
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
    "got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
    "penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_search.py:921: UserWarning: One or more of the test scores are non-finite: [       nan 0.79678362        nan 0.75167265 0.78508932        nan
        nan        nan 0.79469484 0.79177165 0.76190732        nan
        nan 0.73224739 0.79720137        nan 0.76984378        nan
 0.79573868        nan        nan 0.79532158        nan        nan
 0.76984466 0.74331756        nan 0.79678362        nan 0.79887217
 0.78550664 0.73183007 0.73433725 0.79678362        nan        nan
 0.7921881  0.7633687  0.79553013 0.79365296 0.77652895 0.76149
 0.73809683        nan        nan 0.77130625 0.76629298 0.79678362
        nan        nan 0.75041852        nan 0.76483051        nan
        nan        nan        nan        nan        nan        nan
 0.76942625 0.7932339         nan        nan 0.77527547        nan
 0.77402199        nan 0.79344223 0.7320384  0.74352611 0.79678362
        nan 0.76378645 0.78863925 0.79678296 0.79615666 0.79490448
 0.79678362 0.79448564        nan 0.77652895 0.791146   0.7339195
        nan 0.79761913        nan        nan 0.79260651 0.7882215
 0.79365296 0.73559095 0.79594942 0.76963502 0.7437351  0.76733704
        nan        nan        nan        nan 0.78655179 0.79281527
 0.77527569 0.75250707        nan 0.75731245        nan        nan
        nan 0.77172466 0.79573934        nan 0.7750667         nan
 0.78070344 0.79678362        nan 0.79511238 0.78613534 0.7320384
 0.73182963        nan 0.79678362 0.79365166 0.79761869        nan
        nan 0.77464829        nan 0.79615666 0.73559117 0.763995
 0.73726154 0.79678362 0.79636739 0.76942625        nan        nan
        nan 0.76879994        nan        nan 0.73329276        nan
 0.79239861        nan 0.78529765 0.79678362        nan        nan
        nan        nan 0.7752759         nan        nan        nan
        nan        nan 0.76942603 0.79678362 0.73350153        nan
        nan        nan 0.76274195 0.76942581 0.791771   0.73183007
        nan        nan        nan 0.7750667         nan 0.79448651
 0.75125468        nan 0.78070475 0.79678362        nan        nan
 0.79636586        nan 0.79740992 0.77005321 0.78676012 0.79699238
        nan 0.75668593        nan        nan 0.79887043 0.77443996
 0.77506692        nan 0.7930247         nan 0.76378645        nan
        nan        nan        nan 0.79678362        nan 0.79469484
        nan 0.76378645 0.79636586 0.79782768 0.79803623 0.77443974
        nan        nan 0.7704714  0.78676012 0.73182963        nan
        nan        nan 0.79782768 0.76963502        nan 0.78634259
        nan 0.7750667  0.79678362 0.79678362 0.79678362 0.79678362
 0.79532093        nan 0.79720137        nan        nan        nan
 0.79678362        nan 0.79323325 0.77443974 0.7621163         nan
        nan 0.79323325 0.76169833 0.77067973 0.76733704        nan
 0.76587522        nan 0.73182963 0.79615666        nan 0.73182963
 0.79553013 0.79678274        nan        nan        nan        nan
        nan 0.79427818        nan 0.79720159        nan 0.73559074
 0.79511347 0.77527547 0.79553013 0.79678362 0.73182963 0.79678362
        nan        nan        nan 0.79260825 0.79156376 0.73433725
 0.78049555        nan        nan 0.79448804        nan 0.73496421
 0.75000164        nan 0.78404613        nan 0.79678362        nan
 0.79177165 0.77360423        nan        nan 0.73182963        nan
 0.79678362 0.73224717        nan        nan 0.77360467        nan
        nan        nan 0.73726154        nan        nan 0.79824565
        nan        nan        nan        nan        nan        nan
 0.75334236        nan        nan 0.77652895 0.77360423        nan
        nan        nan 0.75689492 0.79490427        nan        nan
        nan 0.785297          nan        nan        nan        nan
        nan 0.76733704        nan        nan 0.7621163  0.76420442
        nan        nan 0.79239817        nan 0.79720137 0.73955842
        nan        nan        nan 0.74331734        nan 0.73747052
 0.75313338 0.76211609 0.7819578         nan        nan 0.75188513
        nan 0.79239752        nan        nan 0.77631822 0.79678362
 0.77506692        nan 0.76253297        nan        nan 0.78070497
 0.79678362 0.79636543        nan 0.73934987        nan 0.78550664
 0.79678362        nan 0.73475479 0.79803666        nan        nan
        nan 0.79260672 0.79573868        nan        nan 0.74707757
 0.79678362        nan 0.79678362        nan        nan        nan
        nan 0.79678362 0.77360423 0.78759519        nan 0.773813
 0.77506692 0.7188764         nan        nan 0.79678296        nan
 0.79532158        nan        nan 0.77443974        nan        nan
 0.78592483 0.75731245        nan 0.78759519 0.79386086        nan
 0.7320384  0.79427905 0.79532137 0.73308399        nan 0.73287522
        nan 0.75188142 0.77464873 0.79385912        nan 0.7873873
        nan        nan 0.77527569 0.74916656        nan 0.73182963
 0.79594723        nan        nan        nan        nan 0.79239839
 0.79782746        nan        nan 0.79761869        nan 0.79678362
        nan 0.73182963        nan 0.79678362        nan        nan
 0.7633687  0.76211674 0.79553035 0.76942581        nan 0.79782746
        nan 0.79532115 0.73579994 0.7493749  0.79532137        nan
        nan 0.76420486 0.79532137        nan        nan 0.77443996
 0.77485771        nan        nan        nan 0.79678362 0.73872335
 0.79678362 0.74436162 0.73559074        nan        nan 0.77631822
 0.79720028 0.73955886        nan 0.7882215  0.79532158 0.73308377
 0.73203884 0.79678362 0.77130625 0.75940144 0.79636543        nan
 0.79657485        nan        nan        nan 0.77318648        nan
        nan        nan]
  category=UserWarning

Wall time: 3min 20s

Out[85]:

RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=500,
                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,
       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,
       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,
       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,
       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),
                                        'max_iter': array([  1,   6,  11,  16,  21,  26,  31,  36,  41,  46,  51,  56,  61,
        66,  71,  76,  81,  86,  91,  96, 101, 106, 111, 116, 121, 126,
       131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191,
       196]),
                                        'penalty': ['none', 'l1', 'l2',
                                                    'elasticnet'],
                                        'solver': ['newton-cg', 'lbfgs',
                                                   'liblinear', 'sag',
                                                   'saga']},
                   verbose=True)

In [86]:

rs_log_reg.score(X_test,Y_test)

rs_log_reg.score(X_test,Y_test)

Out[86]:

0.8013355592654424

In [87]:

rs_log_reg.best_params_

rs_log_reg.best_params_

Out[87]:

{'solver': 'liblinear',
 'penalty': 'l1',
 'max_iter': 46,
 'C': 0.615848211066026}

☝ These are the best params we can get for Logistic Regression model with RandomizedSearchCV with a score of 👉 0.8013355592654424 Trained for => 3min 20s

In [88]:

%%time
np.random.seed(42)

# Setting up random hyperparameter search for RandomForestClassifier()
rs_rf = RandomizedSearchCV(RandomForestClassifier(),
                           param_distributions=rf_grid,
                           cv=5,
                           n_iter=500,
                           verbose=True)

# Fitting random hyperparameter search model for RandomForestClassfier()
rs_rf.fit(X_train, Y_train)

%%time np.random.seed(42) # Setting up random hyperparameter search for RandomForestClassifier() rs_rf = RandomizedSearchCV(RandomForestClassifier(), param_distributions=rf_grid, cv=5, n_iter=500, verbose=True) # Fitting random hyperparameter search model for RandomForestClassfier() rs_rf.fit(X_train, Y_train)

Fitting 5 folds for each of 500 candidates, totalling 2500 fits

F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_validation.py", line 593, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 393, in fit
    for i, t in enumerate(trees))
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 902, in fit
    X_idx_sorted=X_idx_sorted)
  File "F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\tree\_classes.py", line 288, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  FitFailedWarning)
F:\ML_Projects\Assignments\env\lib\site-packages\sklearn\model_selection\_search.py:921: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan 0.80012674        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78884889        nan        nan        nan        nan 0.79950044
        nan 0.75835302 0.77652524        nan        nan 0.76294832
        nan        nan 0.79281767        nan 0.73182963        nan
 0.79991776        nan        nan        nan        nan 0.75919158
        nan        nan        nan        nan        nan 0.80096269
        nan        nan 0.78863991        nan        nan 0.79950044
        nan        nan        nan        nan        nan        nan
        nan 0.79762043        nan        nan        nan        nan
        nan 0.80096378        nan 0.79636739        nan 0.79741276
        nan        nan 0.79344267        nan 0.74707626        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79657703        nan        nan
        nan        nan        nan 0.79866406        nan        nan
        nan        nan        nan        nan 0.79678493        nan
        nan        nan        nan        nan 0.79929167        nan
        nan 0.79908247        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79741167        nan        nan        nan        nan        nan
 0.7942808  0.79970943        nan        nan        nan        nan
 0.7974121         nan        nan        nan        nan        nan
        nan        nan 0.78675903        nan        nan 0.80054559
 0.78843223        nan        nan        nan        nan        nan
        nan        nan        nan 0.79929211        nan        nan
        nan        nan        nan        nan 0.79365187        nan
 0.79448607        nan 0.76608268        nan 0.79971117        nan
        nan        nan        nan        nan        nan 0.73182963
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7995            nan        nan
        nan        nan        nan        nan        nan        nan
        nan 0.79615906        nan        nan        nan        nan
        nan        nan        nan 0.79239861        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79365078        nan        nan        nan        nan        nan
        nan 0.79615971        nan        nan 0.76733486        nan
 0.80033595        nan        nan 0.79824848        nan 0.79845682
        nan        nan 0.76002579        nan 0.78968419        nan
 0.7957413         nan        nan        nan        nan 0.79803884
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan 0.7921905         nan
        nan        nan        nan 0.79636717        nan        nan
 0.79636739        nan        nan        nan        nan 0.7978292
        nan        nan        nan        nan        nan 0.79615906
 0.79762087 0.79887413        nan        nan 0.78738686        nan
        nan        nan        nan        nan        nan 0.78926469
        nan        nan 0.79678558        nan        nan        nan
        nan        nan        nan        nan        nan 0.79783008
        nan 0.79887283        nan 0.79448891 0.79762131        nan
        nan        nan        nan        nan        nan        nan
 0.79803906        nan 0.79678427        nan        nan 0.79741101
 0.79929167 0.77631516        nan        nan        nan        nan
        nan        nan 0.79741188        nan        nan        nan
        nan 0.79741254        nan        nan 0.79929298        nan
        nan        nan        nan        nan        nan        nan
 0.79783008        nan 0.7992932         nan 0.79678645 0.80430429
        nan        nan        nan        nan        nan        nan
        nan 0.79636804        nan        nan        nan        nan
        nan        nan        nan        nan 0.79824718        nan
        nan        nan        nan 0.7967858         nan        nan
        nan        nan        nan        nan 0.79386086        nan
        nan 0.79929124 0.8003366         nan        nan        nan
        nan        nan        nan 0.79741145        nan        nan
        nan        nan        nan        nan 0.79448782        nan
        nan        nan        nan        nan        nan        nan
        nan        nan 0.78801339        nan        nan 0.79553057
 0.79950088        nan        nan        nan        nan        nan
 0.79574087 0.74582322        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79845594        nan        nan
        nan        nan        nan 0.79845551        nan        nan
        nan        nan        nan 0.79991776        nan        nan
        nan        nan        nan        nan        nan 0.79866428
        nan        nan        nan        nan        nan        nan
 0.78237402        nan        nan        nan        nan 0.80138044
        nan        nan        nan 0.8011708  0.79991841        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73182963        nan        nan 0.79929167 0.79657725 0.79135324
 0.79072759        nan        nan        nan        nan        nan
        nan        nan]
  category=UserWarning

Wall time: 12min 50s

Out[88]:

RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=500,
                   param_distributions={'max_depth': [None, 3, 5, 10],
                                        'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
       69, 70, 71, 72, 73, 7...
       408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,
       421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,
       434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,
       447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,
       460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,
       473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485,
       486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498,
       499])},
                   verbose=True)

In [89]:

# Evaluating the Randomized search RandomForestClassifier model
rs_rf.score(X_test, Y_test)

# Evaluating the Randomized search RandomForestClassifier model rs_rf.score(X_test, Y_test)

Out[89]:

0.7929883138564274

In [90]:

# Finding the best parameteres
rs_rf.best_params_

# Finding the best parameteres rs_rf.best_params_

Out[90]:

{'n_estimators': 135,
 'min_samples_split': 61,
 'min_samples_leaf': 3,
 'max_features': 14,
 'max_depth': 10}