Comparar métodos

	Elastic Net	Regresión Logística	Regresión Ridge
Campo	Aprendizaje automático	Estadística para la investigación	Aprendizaje automático
Familia	Machine learning	Process / pipeline	Machine learning
Año de origen	2005	1958	1970
Autor original	Zou, H. & Hastie, T.	David Roxbee Cox	Hoerl, A.E. & Kennard, R.W.
Tipo	Regularized linear regression (L1 + L2 penalty)	Method	L2-regularized linear regression
Fuente seminal	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	logit model, binomial logistic regression, LR	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Relacionados	4	3	4
Resumen	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.