Comparer des méthodes

	Empilement robuste ensembliste	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992 (stacking); robust variants 2000s–present	1990–1997
Auteur d'origine	Wolpert, D. H. (stacking); robust extensions by multiple authors	Schapire, R. E.; Freund, Y.
Type	Ensemble (stacking with robust meta-learner)	Sequential ensemble (iterative reweighting)
Source fondatrice	Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	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.
Alias	robust stacking, robust stacked generalization, outlier-resistant stacking, stacking with robust meta-learner	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	5	6
Résumé	Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.