第13章集成学习和随机森林学习笔记中 oob

13-4 oob（Out-of-Bag）和关于Bagging的更多讨论

oob

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True)
bagging_clf.fit(X, y)

oob-score为true才能记录哪些在训练中用了，哪些没用

n_jobs

%%time
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True)
bagging_clf.fit(X, y)

bootstrap_features

random_subspaces_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=500,
                               bootstrap=True, oob_score=True,
                               max_features=1, bootstrap_features=True)
random_subspaces_clf.fit(X, y)
random_subspaces_clf.oob_score_