def evaluation_error(y_real, y_pred, max_rating, min_rating):
 mae = mean_absolute_error(y_real, y_pred)
 nmae = normalized_mean_absolute_error(y_real, y_pred, max_rating, min_rating)
 rmse = root_mean_square_error(y_real, y_pred)
 return (mae, nmae, rmse)

def evaluation_error ( y_real , y_pred , max_rating , min_rating ) : mae = mean_absolute_error ( y_real , y_pred ) nmae = normalized_mean_absolute_error ( y_real , y_pred , max_rating , min_rating ) rmse = root_mean_square_error ( y_real , y_pred ) return ( mae , nmae , rmse )
it computes the nmae .