如何使用MultiOutputRegressor包装器对XGBoost参数进行网格搜索

from sklearn.multioutput import MultiOutputRegressor
from sklearn.model_selection import GridSearchCV
from xgboost import XGBRegressor

xgb_model = XGBRegressor()

multioutput_model = MultiOutputRegressor(xgb_model)

param_grid = {
    'estimator__learning_rate': [0.1, 0.01],
    'estimator__max_depth': [3, 5, 7],
    'estimator__n_estimators': [100, 200, 300]
}

grid_search = GridSearchCV(multioutput_model, param_grid)

grid_search.fit(X, y)

print("Best parameters: ", grid_search.best_params_)
print("Best score: ", grid_search.best_score_)