방법 비교

	앙상블 의사결정나무	배깅 (Bootstrap Aggregating)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996–2000	1996
창시자	Breiman, L.; Dietterich, T. G.	Breiman, L.
유형	Ensemble (multiple decision trees combined)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
원전	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
별칭	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
관련	6	5
요약	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.