Random Forest Feature Importance Evaluation

This function uses a random forest model to evaluate the importance of features. It fits a random forest classifier on the data and uses Eli5's PermutationImportance to calculate the random importance scores for each feature.

Technology Stack : scikit-learn, eli5

Code Type : Machine Learning Model Evaluation

Code Difficulty : Intermediate

                
                    
def random_feature_importance(X, y):
    from sklearn.ensemble import RandomForestClassifier
    from eli5.sklearn import PermutationImportance

    # Initialize the random forest classifier
    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    
    # Fit the classifier
    clf.fit(X, y)
    
    # Initialize PermutationImportance
    perm = PermutationImportance(clf, random_state=42)
    
    # Fit the permutation importance model
    perm.fit(X, y)
    
    # Return the importance scores
    return perm.importances_mean_