Exercise

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().

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Create a recursive feature eliminator that will select the 2 most important features using a random forest model.