sklearn中有没有设置惩罚阈值的参数？

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
model = LogisticRegression()
model.fit(X_train, y_train)

# 预测概率
proba = model.predict_proba(X_test)

# 自定义阈值
threshold = 0.6

# 根据阈值进行分类
predictions = (proba[:, 1] > threshold).astype(int)

# 打印分类结果
print(predictions)