randomforestregressor 调参 代码

可以通过GridSearchCV函数进行调参，具体代码如下：

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV

# 创建随机森林模型
rf_reg = RandomForestRegressor()

# 设置调参范围
param_grid = {
    'n_estimators': [100, 200, 300],
    'criterion': ['mse', 'mae'],
    'max_depth': [10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

# 使用GridSearchCV进行调参
grid_search = GridSearchCV(estimator=rf_reg,
                           param_grid=param_grid,
                           cv=5)

# 输入数据并训练模型
grid_search.fit(X_train, y_train)

# 输出最佳参数
print(grid_search.best_params_)