Porównaj metody

	Regresja LASSO bayesowska	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Statystyka	Uczenie maszynowe
Rodzina	Regression model	Machine learning
Rok powstania	2008	1970
Twórca	Park & Casella	Hoerl, A.E. & Kennard, R.W.
Typ	Bayesian regularized regression	L2-regularized linear regression
Źródło pierwotne	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Inne nazwy	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	5	4
Podsumowanie	Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals.	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.