Comparer des méthodes

	Régression Linéaire d'Ensemble	Régression Ridge
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1970
Auteur d'origine	Breiman, L. (bagging framework)	Hoerl, A.E. & Kennard, R.W.
Type	Ensemble of linear models	L2-regularized linear regression
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	6	4
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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.