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().
Este ejercicio forma parte del curso
Dimensionality Reduction in Python
Ejercicio interactivo práctico
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# Wrap the feature eliminator around the random forest model
rfe = ____(estimator=____, n_features_to_select=____, verbose=1)