def evaluate(model, X, y, cv):
    cv_results = cross_validate(model, X, y, cv=ts_cv, scoring=['neg_mean_absolute_error', 'neg_root_mean_squared_error'])
    mae = (- cv_results['test_neg_mean_absolute_error'])
    rmse = (- cv_results['test_neg_root_mean_squared_error'])
    print(f'''Mean Absolute Error:     {mae.mean():.3f} +/- {mae.std():.3f}
Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}''')
def evaluate(model, X, y, cv):
    cv_results = cross_validate(model, X, y, cv=ts_cv, scoring=['neg_mean_absolute_error', 'neg_root_mean_squared_error'])
    mae = (- cv_results['test_neg_mean_absolute_error'])
    rmse = (- cv_results['test_neg_root_mean_squared_error'])
    print(f'Mean Absolute Error:     {mae.mean():.3f} +/- {mae.std():.3f}
Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}')