방법 비교

	배깅 앙상블	AdaBoost	부스팅 앙상블
분야	앙상블 학습	머신러닝	앙상블 학습
계열	Machine learning	Machine learning	Machine learning
기원 연도	1996	1997	1990
창시자	Leo Breiman	Freund, Y. & Schapire, R.E.	Robert Schapire
유형	parallel ensemble	Ensemble (sequential boosting of weak learners)	sequential ensemble
원전	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	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.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
별칭	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	adaptive boosting, sequential ensemble
관련	4	5	4
요약	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.