How to do recursive feature elimination in Python?

How to do recursive feature elimination in Python?

How to do recursive feature elimination in Python?

This recipe helps you do recursive feature elimination in Python


Recipe Objective

To increse the score of the model we need to remove the features which are recursive. Removing recursive feature reduces the computational cost and increase the efficiency.

So this is the recipe on How we can do recursive feature elimination in Python.

Step 1 - Import the library

from sklearn.datasets import make_regression from sklearn.feature_selection import RFECV from sklearn import linear_model

We have only imported datasets to import the datasets, RFECV and liner_model.

Step 2 - Setting up the Data

We have imported inbuilt boston dataset and stored data in X and target in y. We have also used print statement to print rows of the dataset. dataset = datasets.load_boston() X = y =

Step 3 - Selecting recursive Features

We have used linear Regression as a model and RFECV is used for recursive feature elimination we have used negative mean squared error as a scoring with cross validation as 4. We have fit and transform rfecv. ols = linear_model.LinearRegression() rfecv = RFECV(estimator=ols, step=1, scoring="neg_mean_squared_error", cv=4, verbose=0, n_jobs=4), y) rfecv.transform(X) print(rfecv) print(rfecv.n_features_) So the output comes as

   estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
   min_features_to_select=1, n_jobs=4, scoring="neg_mean_squared_error",
   step=1, verbose=0)


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