Compara mètodes

	Boosting Ensemble	AdaBoost	Bagging Ensemble
Camp	Aprenentatge per conjunts	Aprenentatge automàtic	Aprenentatge per conjunts
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1990	1997	1996
Autor original	Robert Schapire	Freund, Y. & Schapire, R.E.	Leo Breiman
Tipus	sequential ensemble	Ensemble (sequential boosting of weak learners)	parallel ensemble
Font seminal	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.
Àlies	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating
Relacionats	4	5	4
Resum	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.