방법 비교

	부스팅 앙상블	AdaBoost	배깅 앙상블	그래디언트 부스팅	다수결 투표
분야	앙상블 학습	머신러닝	앙상블 학습	머신러닝	앙상블 학습
계열	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
기원 연도	1990	1997	1996	2001	1996
창시자	Robert Schapire	Freund, Y. & Schapire, R.E.	Leo Breiman	Friedman, J. H.	Leo Breiman
유형	sequential ensemble	Ensemble (sequential boosting of weak learners)	parallel ensemble	Ensemble (sequential boosting of decision trees)	voting aggregation
원전	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
별칭	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	hard voting
관련	4	5	4	5	5
요약	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.	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.	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.