RandomForestRegressor使用RandomizedSearchCV 参数调优代码

以下是使用RandomForestRegressor和RandomizedSearchCV进行参数调优的代码示例：

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint as sp_randint

# 准备数据
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义模型
rf = RandomForestRegressor(random_state=42)

# 定义参数空间
param_dist = {'n_estimators': sp_randint(10, 1000),
              'max_features': sp_randint(1, X_train.shape[1]),
              'min_samples_split': sp_randint(2, 50),
              'min_samples_leaf': sp_randint(1, 50)}

# 进行随机搜索
n_iter_search = 50
random_search = RandomizedSearchCV(rf, param_distributions=param_dist,
                                   n_iter=n_iter_search, cv=5, iid=False, random_state=42)

# 训练模型并进行预测
random_search.fit(X_train, y_train)
y_pred = random_search.predict(X_test)

# 输出最佳参数和评分
print("Best parameters found: ", random_search.best_params_)
print("Best score found: ", random_search.best_score_)

以上代码中，我们使用了RandomForestRegressor作为模型，使用RandomizedSearchCV进行参数调优。我们定义了参数空间param_dist，然后设置了随机搜索的迭代次数n_iter_search，并进行了训练和预测。最后，我们输出了最佳参数和评分