I'm running a random forest classifier in Python (two classes). I am using the feature_importances_ method of the RandomForestClassifier to get feature importances.

It provides a nice visualization of importances but it does not offer insight into which features were most important for each class. For example, it may be for class 1 that some feature values were important, whereas for class 2 some other feature values were more important.

Is it possible to split feature important based on the predicted class?


1 Answer 1


You can use sklearn.inspection.permutation_importance for this purpose

Split your test data by predicted labels and pass to the method

Code example

from sklearn.inspection import permutation_importance
import matplotlib.pyplot as plt
import seaborn as sns

model = RandomForestClassifier()
model.fit(x_train, y_train)

def plot_feature_importances(model, x, y, title):
    result = permutation_importance(model, x, y, n_repeats=100, random_state=0)
    df = pd.DataFrame({'feature_name': x.columns, 'feature_importance': result.importances_mean})
    plt.figure(figsize=(8, 2))
    sns.barplot(data=df, x='feature_importance', y='feature_name')

plot_feature_importances(model, x_test, y_test, 'All test data')

y_pred = model.predict(x_test)
plot_feature_importances(model, x_test[y_pred == 1], y_test[y_pred == 1], 'Predicted as "1"')
plot_feature_importances(model, x_test[y_pred == 0], y_test[y_pred == 0], 'Predicted as "0"')

enter image description here


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.