Compara mètodes

	Regressió amb Elastic Net	Regressió logística regularitzada
Camp	Estadística	Aprenentatge automàtic
Família	Regression model	Machine learning
Any d'origen	2005	1996–2005
Autor original	Hui Zou and Trevor Hastie	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Tipus	Penalized linear regression	Penalized classification model
Font seminal	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 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.
Àlies	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Relacionats	6	5
Resum	Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.	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.