提取Bagging集合的成员

Question

我使用了BaggingRegressor类来构建具有以下参数的最佳模型：

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

有了上面的设置，它将创建10棵树。我想分别提取和访问集成回归的每个成员(每个树)，然后在每个成员上拟合一个测试样本。是否可以访问每个模型？

Answer 1

拟合模型的estimators_属性提供了一个带有集成估计器的列表；以下是一个带有虚拟数据和n_estimators=3的示例，以求简洁：

from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.datasets import make_regression

X, 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(基本估计器在拟合之前不存在)，您可以使用data Xs, ys简单地访问用于拟合的基本估计器，如下所示：

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