Compara mètodes

	Regressió Lasso	Elastic Net
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1996	2005
Autor original	Tibshirani, R.	Zou, H. & Hastie, T.
Tipus	Regularized linear regression (L1 penalty)	Regularized linear regression (L1 + L2 penalty)
Font seminal	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.
Àlies	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
Relacionats	4	4
Resum	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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.