Comparer des méthodes

	Elastic Net	Régression Lasso
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2005	1996
Auteur d'origine	Zou, H. & Hastie, T.	Tibshirani, R.
Type	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)
Source fondatrice	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	4	4
Résumé	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.