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Recursive Feature Elimination with random forests

You'll wrap a Recursive Feature Eliminator around a random forest model to remove features step by step. This method is more conservative compared to selecting features after applying a single importance threshold. Since dropping one feature can influence the relative importances of the others.

You'll need these pre-loaded datasets: X, X_train, y_train.

Functions and classes that have been pre-loaded for you are: RandomForestClassifier(), RFE(), train_test_split().

This exercise is part of the course

Dimensionality Reduction in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Wrap the feature eliminator around the random forest model
rfe = ____(estimator=____, n_features_to_select=____, verbose=1)
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