def METHOD_NAME(model, X_train, X_test, y_train, y_test):
    """
    Train classical ML regression model
    Args:
        model (sklearn): sklearn model that user wants to train
        X_train (pd.DataFrame): Train dataset features
        X_test (pd.DataFrame): Test dataset features
        y_train (pd.DataFrame): target value corresponding to each row in X_train
        y_test (pd.DataFrame): target value corresponding to each row in X_test
    """
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_pred_train = model.predict(X_train)
    # create popular metrics
    train_rmse = mean_squared_error(y_true=y_train, y_pred=y_pred_train, squared=False)
    train_mape = mean_absolute_percentage_error(y_true=y_train, y_pred=y_pred_train)
    test_rmse = mean_squared_error(y_true=y_test, y_pred=y_pred, squared=False)
    test_mape = mean_absolute_percentage_error(y_true=y_test, y_pred=y_pred)
    print(
        f"Regression Root Mean Squared Error => test: {test_rmse.round(4)}\t train: {train_rmse.round(4)}"
    )
    print(
        f"Regression Mean Absolute Percentage Error:  => test: {test_mape.round(4)*100}%\t train: {train_mape.round(4)*100}%"
    )
    return {}