from sklearn.model_selection import cross_val_score

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

scores = cross_val_score(clf,X,y,cv=5)

#confusion matrix

from sklearn.metrics import confusion_matrix

y_true = [2,0,2,2,0,1]

y_pred = [0,0,2,2,0,2]

confusion_matrix(y_true,y_pred)

#classification report

from sklearn.metrics import classification_report

y_true = [0,1,2,2,0]

y_pred = [0,0,2,1,0]

target_names = ['class 0','class 1','class 2']

print(classification_report(y_true,y_pred,target_names=target_names))

#ROC

import numpy as np

from sklearn.metrics import roc_curve

y = np.array([1,1,2,2])

scores = np.array([0.1,0.4,0.35,0.8])

fpr,tpr,thresholds = roc_curve(y,scores,pos_label=2)

fpr

tpr

thresholds

import numpy as np

from sklearn.metrics import roc_auc_score

y_true = np.array([0,0,1,1])

y_scores = np.array([0.1,0.4,0.35,0.8])

roc_auc_score(y_true,y_scores)

參考資料：

1、交叉驗證 - Vancuicide - 部落格園 (cnblogs.com)

2、模型選擇之交叉驗證 - 知乎 (zhihu.com)