방법 비교

	부스팅 앙상블	그래디언트 부스팅	다수결 투표
분야	앙상블 학습	머신러닝	앙상블 학습
계열	Machine learning	Machine learning	Machine learning
기원 연도	1990	2001	1996
창시자	Robert Schapire	Friedman, J. H.	Leo Breiman
유형	sequential 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.	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	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	hard voting
관련	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.	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.