Automatic Recursive Feature Elimination

Now let's automate this recursive process. Wrap a Recursive Feature Eliminator (RFE) around our logistic regression estimator and pass it the desired number of features.

All the necessary functions and packages have been pre-loaded and the features have been scaled for you.

This exercise is part of the course

Dimensionality Reduction in Python

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Exercise instructions

Create the RFE with a LogisticRegression() estimator and 3 features to select.
Print the features and their ranking.
Print the features that are not eliminated.

Hands-on interactive exercise

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

# Create the RFE with a LogisticRegression estimator and 3 features to select
rfe = ____(estimator=____, n_features_to_select=____, verbose=1)

# Fits the eliminator to the data
rfe.fit(X_train, y_train)

# Print the features and their ranking (high = dropped early on)
print(dict(zip(X.columns, rfe.____)))

# Print the features that are not eliminated
print(X.columns[rfe.____])

# Calculates the test set accuracy
acc = accuracy_score(y_test, rfe.predict(X_test))
print(f"{acc:.1%} accuracy on test set.")

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