Módszerek összehasonlítása

	Bayesian Ridge regresszió	Elastic Net	Lasso-regresszió
Tudományterület	Gépi tanulás	Gépi tanulás	Gépi tanulás
Módszercsalád	Bayesian methods	Machine learning	Machine learning
Keletkezés éve	1992	2005	1996
Megalkotó	MacKay, D. J. C.	Zou, H. & Hastie, T.	Tibshirani, R.
Típus	Probabilistic regularised regression	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)
Alapmű	MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.	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.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alternatív nevek	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Kapcsolódó	3	4	4
Összefoglaló	Bayesian Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty.	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.	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.