Comparar métodos

	Regresión Elastic Net	Regresión logística regularizada
Campo	Estadística	Aprendizaje automático
Familia	Regression model	Machine learning
Año de origen	2005	1996–2005
Autor original	Hui Zou and Trevor Hastie	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Tipo	Penalized linear regression	Penalized classification model
Fuente 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.
Alias	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
Relacionados	6	5
Resumen	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.