There is a way to set the threshold cross_val_score sklearn?

I've trained a model, then I adjust the threshold to 0.22. The model in the following below :

# Try with Threshold
pred_proba = LGBM_Model.predict_proba(X_test)# Adjust threshold for predictions proba
prediction_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))

then I want to validate this model using cross_val_score. I've searched but can't find the method to set threshold for cross_val_score. The cross_val_score that I've used like the following below :

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 ??

Or there is other method to validate this model using threshold?

Assuming that you are working with a two-class classification problem you could override the predict method of LGBMClassifier object with your thresholding approach as shown below:

import numpy as np
from lightgbm import LGBMClassifier
from 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])