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	Regularyzowana regresja logistyczna	Elastic Net
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1996–2005	2005
Twórca	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Zou, H. & Hastie, T.
Typ	Penalized classification model	Regularized linear regression (L1 + L2 penalty)
Źródło pierwotne	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.
Inne nazwy	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression
Pokrewne	5	4
Podsumowanie	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.	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.