由于我的数据集不平衡，以下方法将KNN分类器与StratifiedShuffleSplit结合使用：
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来计算recall和precision指标而不是默认精度？
谢谢
您需要：
sklearn.metrics.recall_score(y_true,y_pred)
sklearn.metrics.precision_score(y_true,y_pred)
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
def 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]) # predict the labels of the test set
y_true = train_y.loc[test] # get the true labels of the test test
score = recall_score(y_true,y_pred) # recall estimation
score2 = precision_score(y_true,y_pred) # precision estimation
scores.append(score)
scores2.append(score2)