Comparer des méthodes

	Régression Linéaire d'Ensemble	Bagging (Bootstrap Aggregating)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1996
Auteur d'origine	Breiman, L. (bagging framework)	Breiman, L.
Type	Ensemble of linear models	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	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Apparentées	6	5
Résumé	Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions.	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.