Comparer des méthodes

	Généralisation empilée	Ensemble par Boosting
Domaine	Apprentissage ensembliste	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	1992	1990
Auteur d'origine	David Wolpert	Robert Schapire
Type	meta-learning aggregation	sequential ensemble
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
Alias	stacking, meta-learning	adaptive boosting, sequential ensemble
Apparentées	3	4
Résumé	Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.	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.