由于我的数据集是不平衡的，以下方法使用了带有StratifiedShuffleSplit的KNN分类器:

def KNN(train_x, train_y):    skf = StratifiedShuffleSplit()    scores = []    for train, test in skf.split(train_x, train_y):        clf = KNeighborsClassifier(n_neighbors=2, n_jobs=-1)        clf.fit(train_x.loc[train], train_y.loc[train])        score = clf.score(train_x.loc[test], train_y.loc[test])        scores.append(score)    res = np.asarray(scores).mean()    print(res)

如何修改scores以计算recall（召回率）和precision（精确率）指标，而不是默认的准确率？

谢谢，

回答：

你需要:

sklearn.metrics.recall_score(y_true, y_pred)sklearn.metrics.precision_score(y_true, y_pred)

from sklearn.metrics import recall_scorefrom sklearn.metrics import precision_scoredef KNN(train_x, train_y):    skf = StratifiedShuffleSplit()    scores = []    scores2 = []    for train, test in skf.split(train_x, train_y):        clf = KNeighborsClassifier(n_neighbors=2, n_jobs=-1)        clf.fit(train_x.loc[train], train_y.loc[train])        y_pred = clf.predict(train_x.loc[test]) # 预测测试集的标签        y_true = train_y.loc[test] # 获取测试集的真实标签        score = recall_score(y_true, y_pred) # 召回率估算        score2 = precision_score(y_true, y_pred) # 精确率估算        scores.append(score)        scores2.append(score2)