我使用了BaggingRegressor类来构建最佳模型，使用的参数如下：

from sklearn.tree import DecisionTreeRegressorfrom sklearn.ensemble import BaggingRegressorReg_ensemble=BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3),n_estimators=10,random_state=0).fit(feature,target)

使用上述设置，它将创建10棵树。我希望单独提取和访问每个回归集成成员（每棵树），然后在每个成员上拟合一个测试样本。这是可能访问每个模型的吗？

回答：

拟合模型的estimators_属性提供了一个包含集成估计器的列表；这里是一个使用虚拟数据和n_estimators=3的简短示例：

from sklearn.tree import DecisionTreeRegressorfrom sklearn.ensemble import BaggingRegressorfrom sklearn.datasets import make_regressionX, y = make_regression(n_samples=100, n_features=4,                    n_informative=2, n_targets=1,                    random_state=0, shuffle=False)regr = BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3),                        n_estimators=3, random_state=0)regr.fit(X, y)regr.estimators_# result:[DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=3,                       max_features=None, max_leaf_nodes=None,                       min_impurity_decrease=0.0, min_impurity_split=None,                       min_samples_leaf=1, min_samples_split=2,                       min_weight_fraction_leaf=0.0, presort='deprecated',                       random_state=2087557356, splitter='best'), DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=3,                       max_features=None, max_leaf_nodes=None,                       min_impurity_decrease=0.0, min_impurity_split=None,                       min_samples_leaf=1, min_samples_split=2,                       min_weight_fraction_leaf=0.0, presort='deprecated',                       random_state=132990059, splitter='best'), DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=3,                       max_features=None, max_leaf_nodes=None,                       min_impurity_decrease=0.0, min_impurity_split=None,                       min_samples_leaf=1, min_samples_split=2,                       min_weight_fraction_leaf=0.0, presort='deprecated',                       random_state=1109697837, splitter='best')]

在拟合BaggingRegressor之后（拟合之前基本估计器是不存在的），您可以简单地以如下方式访问基本估计器以使用数据Xs, ys进行拟合：

for model in regr.estimators_:    model.fit(Xs, Ys)