K折交叉驗證(K-fold cross-validation): 將樣本分成K份，每份數量大致相等，然後用其他的某一份作為測試，其他樣本作為訓練集，得到一個模型和一組預測值及模型評估值；迴圈這個過程K次，得到K組模型評估值，對其取平均值即得到最終的評估結果

from sklearn.model_selection import cross_val_score

clf = svm.SVC(kernel='linear', C=1)

scores = cross_val_score(clf, iris.data, iris.target, cv=5)

scores  

>>> 

#5次交叉驗證的得分                                         

array([ 0.96...,  1.  ...,  0.96...,  0.96...,  1.        ])

#這種資料切分方式可以打亂順序

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import ShuffleSplit

cv = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0)

cross_val_score(clf, iris.data, iris.target, cv=cv)

通過交叉驗證獲取預測

from sklearn import metrics

rom sklearn.model_selection import cross_val_predict

predicted = cross_val_predict(clf, iris.data, iris.target, cv=10)

metrics.accuracy_score(iris.target, predicted)

>>> 

0.966