Comparer des méthodes

	Elastic Net	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2005	2001
Auteur d'origine	Zou, H. & Hastie, T.	Breiman, L.
Type	Regularized linear regression (L1 + L2 penalty)	Ensemble (bagging of decision trees)
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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.