Compara mètodes

	Generalització apilada	Boosting Ensemble
Camp	Aprenentatge per conjunts	Aprenentatge per conjunts
Família	Machine learning	Machine learning
Any d'origen	1992	1990
Autor original	David Wolpert	Robert Schapire
Tipus	meta-learning aggregation	sequential ensemble
Font seminal	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.
Àlies	stacking, meta-learning	adaptive boosting, sequential ensemble
Relacionats	3	4
Resum	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.