How to do recursive feature elimination in Python (DecisionTreeRegressor)?
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# How to do recursive feature elimination in Python (DecisionTreeRegressor)?

This recipe helps you do recursive feature elimination in Python (DecisionTreeRegressor)

0
This data science python source code does the following: 1. Imports necessary Sklearn dataset and Sklearn modules. 2. Obtains most optimum number of features from RFECV using Decision tree Regressor. 3. Performs Cross Validation to avoid overfitting.
In [1]:
```## How to do recursive feature elimination in Python (DecisionTreeRegressor)
def Snippet_129():
print()
print(format('How to do recursive feature elimination in Python (DecisionTreeRegressor)','*^82'))

import warnings
warnings.filterwarnings("ignore")

from sklearn.datasets import make_regression
from sklearn.feature_selection import RFECV
from sklearn.tree import DecisionTreeRegressor

# Create Data
# Generate features matrix, target vector, and the true coefficients
X, y = make_regression(n_samples = 10000, n_features = 100, n_informative = 2)
print(); print(X.shape)

# Create Linear Model
dtree = DecisionTreeRegressor()

# Create recursive feature eliminator that scores features by mean squared errors
rfecv = RFECV(estimator=dtree, step=1, scoring='neg_mean_squared_error', cv=4, verbose=1,
n_jobs = 4)

# Fit recursive feature eliminator
rfecv.fit(X, y)

# Recursive feature elimination
rfecv.transform(X)

# Number of best features
print(); print(rfecv)
print(); print(rfecv.n_features_)

Snippet_129()
```
```****How to do recursive feature elimination in Python (DecisionTreeRegressor)*****

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RFECV(cv=4,
estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best'),
min_features_to_select=1, n_jobs=4, scoring='neg_mean_squared_error',
step=1, verbose=1)

2
```

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