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

## Recipe Objective

While training a model there are many feature which are recursive and it effects the computational cost.

So this is the recipe on how we can do recursive feature elimination in Python (DecisionTreeRegressor).

## Step 1 - Importing Library

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

We have only imported make_regression, RFECV, DecisionTreeRegressor which is needed.

## Step 2 - Creating dataset

We have created a dataset by function make_regression with desired parameters. ``` X, y = make_regression(n_samples = 10000, n_features = 100, n_informative = 2) print(X.shape) ```

## Step 3 - Using recursive feature eliminator

.

• We are using DecisionTreeRegressor as a model
• ``` dtree = DecisionTreeRegressor() ```
• Using Recursive feature elemination. We have passed the required parameters as model, step, scoring, cv and verbose. We have fited the data and transformed it to remove the recursive feature.
• ``` rfecv = RFECV(estimator=dtree, step=1, scoring="neg_mean_squared_error", cv=4, verbose=1, n_jobs = 4) rfecv.fit(X, y) rfecv.transform(X) print(rfecv) print(rfecv.n_features_) ``` So the output comes as:

```(10000, 100)
Fitting estimator with 100 features.
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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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