由于我有一个不平衡的数据集,因此以下方法使用 KNN 分类器和 StratifiedShuffleSplit:
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)
如何修改分数
来计算召回率
和精度
指标而不是默认准确率?
谢谢,
最佳答案
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)
关于python-3.x - 使用 StratifiedShuffleSplit 时计算召回率指标,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/59007270/