Comparer des méthodes

	Robust Bagging	Bagging (Bootstrap Aggregating)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2000s	1996
Auteur d'origine	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Breiman, L.
Type	Ensemble (robust bootstrap aggregating)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Alias	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Apparentées	6	5
Résumé	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.