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	Regularyzacja grzbietowa (Ridge Regression)	Regresja Lasso
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1970	1996
Twórca	Hoerl, A.E. & Kennard, R.W.	Tibshirani, R.
Typ	L2-regularized linear regression	Regularized linear regression (L1 penalty)
Źródło pierwotne	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Inne nazwy	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Pokrewne	4	4
Podsumowanie	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.	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.