PYTHON

sklearn cross validation score

from sklearn.model_selection import cross_val_score
scores = cross_val_score(classifier_logreg, X_train, y_train, cv = 5, scoring='accuracy')
print('Cross-validation scores:{}'.format(scores))
print('Average cross-validation score: {}'.format(scores.mean()))

sklearn cross validation score

from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import cross_val_score

clf = RidgeClassifier() # estimator
score = cross_val_score(clf, X, y, cv=5)

# By default, the score computed at each CV iteration is the score
# method of the estimator. It is possible to change this by using 
# the scoring parameter:

scores = cross_val_score(clf, X, y, cv=5, scoring='f1_macro')

cross validation sklearn

>>> clf = svm.SVC(kernel='linear', C=1)
>>> scores = cross_validation.cross_val_score(
...    clf, iris.data, iris.target, cv=5)
...
>>> scores                                              
array([ 0.96...,  1.  ...,  0.96...,  0.96...,  1.        ])