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().
Questo esercizio fa parte del corso
Dimensionality Reduction in Python
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Wrap the feature eliminator around the random forest model
rfe = ____(estimator=____, n_features_to_select=____, verbose=1)