Scikit学习库将临界点转化为逻辑回归

Question

我正在尝试使用Scikit学习库将截止点更改为逻辑回归，但是即使阅读了文档，我也看不到。在SPSS中，它为您提供了更改该参数的选项，但是在这里我不明白。我把算法代码。有什么帮助吗？谢谢

X = np.array(dataS)
y = np.array(target)
X.shape
from sklearn import linear_model
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sb
import warnings 
warnings.filterwarnings("ignore")
model = linear_model.LogisticRegression()
model.fit(X,y)
predictions = model.predict(X)
model.score(X,y)

validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, y, 
test_size=validation_size, random_state=seed)
name='Logistic Regression'
kfold = model_selection.KFold(n_splits=161, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)

predictions = model.predict(X_validation)
print(accuracy_score(Y_validation, predictions))

print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))