In sklearn, the classification version of Random Forest is implemented using the RandomForestClassifier:

You will also calculate the cross-validation accuracy using the cross_val_score() function:

In the end, you'll print the importance of each feature. The feature_importances_ attribute returns an array of importance scores — these scores represent how much each feature contributed to reducing Gini impurity across all the decision nodes where that feature was used. In other words, the more a feature helps split the data in a useful way, the higher its importance.

However, the attribute only gives the scores without feature names. To display both, you can pair them using Python’s zip() function:

for feature, importance in zip(X.columns, model.feature_importances_):
    print(feature, importance)

This prints each feature name along with its importance score, making it easier to understand which features the model relied on most.