방법 비교

	부스팅	배깅 (Bootstrap Aggregating)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1990–1997	1996
창시자	Schapire, R. E.; Freund, Y.	Breiman, L.
유형	Sequential ensemble (iterative reweighting)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
원전	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
별칭	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
관련	6	5
요약	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.