Confronta i metodi

	Boosting Ensemble	[UNTRANSLATED: AdaBoost]	Gradient Boosting
Campo	Apprendimento ensemble	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1990	1997	2001
Ideatore	Robert Schapire	Freund, Y. & Schapire, R.E.	Friedman, J. H.
Tipo	sequential ensemble	Ensemble (sequential boosting of weak learners)	Ensemble (sequential boosting of decision trees)
Fonte seminale	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Correlati	4	5	5
Sintesi	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.	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.