在我的问题中，我想使用一个简单的RandomizedSearchCV调优器来调优sklearn.ensemble.StackingRegressor。由于在实例化StackingRegressor()时需要定义estimators，我无法正确地在param_distribution随机搜索中定义estimators的参数空间。

我尝试了以下方法，但遇到了错误：

from sklearn.datasets import load_diabetesfrom sklearn.linear_model import RidgeCVfrom sklearn.svm import LinearSVRfrom sklearn.model_selection import RandomizedSearchCVfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressorfrom sklearn.ensemble import StackingRegressorX, y = load_diabetes(return_X_y=True)rfr = RandomForestRegressor()gbr = GradientBoostingRegressor()estimators = [rfr, gbr]sreg = StackingRegressor(estimators=estimators)params = {'rfr__max_depth': [3, 5, 10, 100],          'gbr__max_depth': [3, 5, 10, 100]}grid = RandomizedSearchCV(estimator=sreg,                           param_distributions=params,                          cv=3)grid.fit(X,y)

我遇到了错误 AttributeError: 'RandomForestRegressor' object has no attribute 'estimators_'。

有没有办法在StackingRegressor中调优不同estimators的参数？

回答：

如果你将你的estimators定义为estimator名称和estimator实例的元组列表，如下所示，你的代码应该可以工作。

import pandas as pdfrom sklearn.datasets import load_diabetesfrom sklearn.model_selection import RandomizedSearchCVfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressorfrom sklearn.ensemble import StackingRegressorX, y = load_diabetes(return_X_y=True)rfr = RandomForestRegressor()gbr = GradientBoostingRegressor()estimators = [('rfr', rfr), ('gbr', gbr)]sreg = StackingRegressor(estimators=estimators)params = {    'rfr__max_depth': [3, 5],    'gbr__max_depth': [3, 5]}grid = RandomizedSearchCV(    estimator=sreg,    param_distributions=params,    n_iter=2,    cv=3,    verbose=1,    random_state=100)grid.fit(X, y)res = pd.DataFrame(grid.cv_results_)print(res)#    mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score# 0       1.121728      0.024188  ...        0.024546                2# 1       1.096936      0.034377  ...        0.013047                1