from sklearn.svm import SVR
from sklearn.model_selection import cross_validate
from sklearn.model_selection import KFold

svr_rbf = SVR(kernel='rbf')
scoring = ['neg_mean_absolute_error', 'neg_mean_squared_error', 'r2']


scores = cross_validate(estimator, X, y, cv=KFold(10, shuffle=True), scoring=scoring, return_train_score=False)

score = -1 * scores['test_neg_mean_absolute_error']
print("MAE: %.4f (%.4f)" % (score.mean(), score.std()))

score = -1 * scores['test_neg_mean_squared_error']
print("MSE: %.4f (%.4f)" % (score.mean(), score.std()))

score = scores['test_r2']
print("R^2: %.4f (%.4f)" % (score.mean(), score.std()))