Sammenlign metoder

	Bayesiansk Ridge-regresjon	Elastic Net
Fagfelt	Maskinlæring	Maskinlæring
Familie	Bayesian methods	Machine learning
Opprinnelsesår	1992	2005
Opphavsperson	MacKay, D. J. C.	Zou, H. & Hastie, T.
Type	Probabilistic regularised regression	Regularized linear regression (L1 + L2 penalty)
Opprinnelig kilde	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.
Alias	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
Relaterte	3	4
Sammendrag	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.