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	Elastic Net-regression	Lasso-regression
Ämnesområde	Statistik	Maskininlärning
Familj	Regression model	Machine learning
Ursprungsår	2005	1996
Upphovsperson	Hui Zou and Trevor Hastie	Tibshirani, R.
Typ	Penalized linear regression	Regularized linear regression (L1 penalty)
Ursprungskälla	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	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Närliggande	6	4
Sammanfattning	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.	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.