默认的，cross_val_score只能计算一个类型的分数，要想获得多个度量值，可用函数cross_validate
>>> 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']
标签：scoring,...,val,macro,cross,score,test From： https://www.cnblogs.com/cupleo/p/17765453.html