如何在打包的DecisionTreeClassifiers上正确使用GridSearchCV

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

# 假设X为特征矩阵，y为目标变量
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

dt = DecisionTreeClassifier()

param_grid = {
    'criterion': ['gini', 'entropy'],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 3]
}

grid_search = GridSearchCV(estimator=dt, param_grid=param_grid, cv=5)

grid_search.fit(X_train, y_train)

best_params = grid_search.best_params_
best_score = grid_search.best_score_
print("Best Parameters: ", best_params)
print("Best Score: ", best_score)

dt_best = DecisionTreeClassifier(**best_params)
dt_best.fit(X_train, y_train)

y_pred = dt_best.predict(X_test)