Comparer des méthodes

	Ensemble d'empilement régularisé	Empilement
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992–1996	1992
Auteur d'origine	Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)	Wolpert, D.H.
Type	Ensemble (stacked generalization with regularized meta-learner)	Ensemble (heterogeneous meta-learning)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Alias	regularized stacked generalization, ridge stacking, lasso meta-learner ensemble, penalized stacking	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Apparentées	6	5
Résumé	Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.