Comparer des méthodes

	Régression logistique ensembliste	Empilement
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2000s	1992
Auteur d'origine	Breiman, L. (bagging); broader ensemble literature	Wolpert, D.H.
Type	Ensemble of logistic regression classifiers	Ensemble (heterogeneous meta-learning)
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Alias	logistic regression ensemble, bagged logistic regression, aggregated logistic regression, logistic ensemble classifier	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Apparentées	6	5
Résumé	Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.	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.