交叉验证 cross_val_score 获得多个度量值

>>> from sklearn.model_selection import cross_validate
>>> from sklearn.metrics import recall_score
>>> scoring = ['precision_macro', 'recall_macro']
>>> clf = svm.SVC(kernel='linear', C=1, random_state=0)
>>> scores = cross_validate(clf, iris.data, iris.target, scoring=scoring,
...                         cv=5)

# 默认是运行和打分时间+测试集的指标
>>> sorted(scores.keys())
['fit_time', 'score_time', 'test_precision_macro', 'test_recall_macro']
>>> scores['test_recall_macro']                       
array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])

# 可以指定return_train_score参数，同时返回训练集的度量指标值
>>> scores = cross_validate(clf, iris.data, iris.target, scoring=scoring,
...                         cv=5, return_train_score=True)
>>> sorted(scores.keys())                 
['fit_time', 'score_time', 'test_prec_macro', 'test_rec_macro',
 'train_prec_macro', 'train_rec_macro']