Comparer des méthodes

	Régression logistique régularisée	Elastic Net
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2005	2005
Auteur d'origine	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Zou, H. & Hastie, T.
Type	Penalized classification model	Regularized linear regression (L1 + L2 penalty)
Source fondatrice	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	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.
Alias	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
Apparentées	5	4
Résumé	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.	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.