Drop-Column Feature Importance Demo

from sklearn.base import clone 

def drop_col_feat_imp(model, X_train, y_train, random_state = 42):
    
    # clone the model to have the exact same specification as the one initially trained
    model_clone = clone(model)
    # set random_state for comparability
    model_clone.random_state = random_state
    # training and scoring the benchmark model
    model_clone.fit(X_train, y_train)
    benchmark_score = model_clone.score(X_train, y_train)
    # list for storing feature importances
    importances = []
    
    # iterating over all columns and storing feature importance (difference between benchmark and new model)
    for col in X_train.columns:
        model_clone = clone(model)
        model_clone.random_state = random_state
        model_clone.fit(X_train.drop(col, axis = 1), y_train)
        drop_col_score = model_clone.score(X_train.drop(col, axis = 1), y_train)
        importances.append( round( (benchmark_score - drop_col_score)/benchmark_score, 4) )
    
    importances_df = pd.DataFrame(X_train.columns, columns=['Feature'])
    importances_df['drop_col_importance'] = importances
    return importances_df