有没有办法设置阈值 cross_val_score sklearn?

我已经训练了一个模型，然后我将阈值调整为0.22。模型如下所示：

# Try with Thresholdpred_proba = LGBM_Model.predict_proba(X_test)# Adjust threshold for predictions probaprediction_with_threshold = []for item in pred_proba[:,0]:    if item > 0.22 :        prediction_with_threshold.append(0)    else:        prediction_with_threshold.append(1)print(classification_report(y_test,prediction_with_threshold))

然后我想使用 cross_val_score 验证这个模型。我已经搜索过，但找不到设置 cross_val_score 阈值的方法。我使用过的 cross_val_score 如下所示：

F1Scores = cross_val_score(LGBMClassifier(random_state=101,learning_rate=0.01,max_depth=-1,min_data_in_leaf=60,num_iterations=200,num_leaves=70),X,y,cv=5,scoring='f1')F1Scores### how to adjust threshold to 0.22 ??

或者有没有其他方法可以使用阈值来验证这个模型？

回答：

假设你正在处理一个二分类问题，你可以像下面这样重写 LGBMClassifier 对象的 predict 方法，以应用你的阈值方法：

import numpy as npfrom lightgbm import LGBMClassifierfrom sklearn.datasets import make_classificationX, y = make_classification(n_features=10, random_state=0, n_classes=2, n_samples=1000, n_informative=8)class MyLGBClassifier(LGBMClassifier):    def predict(self,X, threshold=0.22,raw_score=False, num_iteration=None,                pred_leaf=False, pred_contrib=False, **kwargs):        result = super(MyLGBClassifier, self).predict_proba(X, raw_score, num_iteration,                                    pred_leaf, pred_contrib, **kwargs)        predictions = [1 if p>threshold else 0 for p in result[:,0]]        return predictionsclf = MyLGBClassifier()clf.fit(X,y)clf.predict(X,threshold=2)  # just testing the implementation# [0,0,0,0,..,0,0,0]        # we get all zeros since we have set threshold as 2F1Scores = cross_val_score(MyLGBClassifier(random_state=101,learning_rate=0.01,max_depth=-1,min_data_in_leaf=60,num_iterations=2,num_leaves=5),X,y,cv=5,scoring='f1')F1Scores#array([0.84263959, 0.83333333, 0.8       , 0.78787879, 0.87684729])