我希望根据随机森林、梯度提升和极端梯度提升的特征重要性来选择特征。我尝试使用随机化网格搜索来拟合我的模型，以获得最佳模型的特征重要性，但它返回了一个我无法理解的错误。以下是我的代码：
gbr = GradientBoostingRegressor(random_state=seed)gbr_params = { "learning_rate": [0.001, 0.01, 0.1], "min_samples_split": [50, 100], "min_samples_leaf": [50, 100], "max_depth":[5, 10, 20]}xgbr = xgboost.XGBRegressor(random_state=seed) xgbr_params = { "learning_rate": [0.001, 0.01, 0.1], "min_samples_leaf": [50, 100], "max_depth":[5, 10, 20], 'reg_alpha': [1.1, 1.2, 1.3], 'reg_lambda': [1.1, 1.2, 1.3]}rfr = RandomForestRegressor(random_state=seed)rfr_params={'n_estimators':[100, 500, 1000], 'max_features':[10,14,18], 'min_samples_split': [50, 100], 'min_samples_leaf': [50, 100],} fs_xgbr = dcv.RandomizedSearchCV(xgbr, xgbr_params, cv=5, iid=False, n_jobs=-1)fs_gbr = dcv.RandomizedSearchCV(gbr, gbr_params, cv=5,iid=False, n_jobs=-1)fs_rfr = dcv.RandomizedSearchCV(rfr, rfr_params, cv=5,iid=False, n_jobs=-1)fs_rfr.fit(X, Y)model = SelectFromModel(fs_rfr, prefit=True)X_rfr = model.transform(X)print('rfr', X_rfr.shape)在X_rfr = model.transform(X)这一行，它返回了以下错误：
ValueError: The underlying estimator RandomizedSearchCV has no `coef_` or `feature_importances_` attribute. Either pass a fitted estimator to SelectFromModel or call fit before calling transform.我不是程序员，在其他地方也未找到解决方案。是否无法使用随机搜索决定的最佳参数来获取模型的特征重要性？
回答：
不要将RandomizedSearchCV类型的对象fs_rfr传递给SelectFromModel，而是传递最佳估计器，例如fs_rfr.best_estimator_。
证明
import xgboostfrom sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressorfrom sklearn.datasets import make_regressionfrom sklearn.model_selection import RandomizedSearchCVfrom sklearn.feature_selection import SelectFromModelseed=42gbr = GradientBoostingRegressor(random_state=seed)gbr_params = { "learning_rate": [0.001, 0.01, 0.1], "min_samples_split": [50, 100], "min_samples_leaf": [50, 100], "max_depth":[5, 10, 20]}xgbr = xgboost.XGBRegressor(random_state=seed) xgbr_params = { "learning_rate": [0.001, 0.01, 0.1], "min_samples_leaf": [50, 100], "max_depth":[5, 10, 20], 'reg_alpha': [1.1, 1.2, 1.3], 'reg_lambda': [1.1, 1.2, 1.3]}rfr = RandomForestRegressor(random_state=seed)rfr_params={'n_estimators':[100, 500, 1000], 'max_features':[10,14,18], 'min_samples_split': [50, 100], 'min_samples_leaf': [50, 100],} fs_xgbr = RandomizedSearchCV(xgbr, xgbr_params, cv=5, iid=False, n_jobs=-1)fs_gbr = RandomizedSearchCV(gbr, gbr_params, cv=5,iid=False, n_jobs=-1)fs_rfr = RandomizedSearchCV(rfr, rfr_params, cv=5,iid=False, n_jobs=-1)X, y = make_regression(1000,10)fs_xgbr.fit(X, y)fs_gbr.fit(X, y)fs_rfr.fit(X, y)model = SelectFromModel(fs_rfr.best_estimator_, prefit=True)X_rfr = model.transform(X)print('rfr', X_rfr.shape)model = SelectFromModel(fs_xgbr.best_estimator_, prefit=True)X_xgbr = model.transform(X)print('xgbr', X_xgbr.shape)model = SelectFromModel(fs_gbr.best_estimator_, prefit=True)X_gbr = model.transform(X)print('gbr', X_gbr.shape)rfr (1000, 3)xgbr (1000, 3)gbr (1000, 4)