Comparer des méthodes

	Ensemble par Boosting	Gradient Boosting
Domaine	Apprentissage ensembliste	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990	2001
Auteur d'origine	Robert Schapire	Friedman, J. H.
Type	sequential ensemble	Ensemble (sequential boosting of decision trees)
Source fondatrice	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.
Alias	adaptive boosting, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	4	5
Résumé	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.