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	Regresja Elastic Net	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Statystyka	Uczenie maszynowe
Rodzina	Regression model	Machine learning
Rok powstania	2005	1970
Twórca	Hui Zou and Trevor Hastie	Hoerl, A.E. & Kennard, R.W.
Typ	Penalized linear regression	L2-regularized linear regression
Źródło pierwotne	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Inne nazwy	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	6	4
Podsumowanie	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.	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.