手法を比較

	バギングアンサンブル	AdaBoost	ブースティングアンサンブル
分野	アンサンブル学習	機械学習	アンサンブル学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1996	1997	1990
提唱者	Leo Breiman	Freund, Y. & Schapire, R.E.	Robert Schapire
種類	parallel ensemble	Ensemble (sequential boosting of weak learners)	sequential ensemble
原典	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.
別名	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	adaptive boosting, sequential ensemble
関連	4	5	4
概要	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.