Comparer des méthodes

	Régression Ridge Bayésienne	Régression Ridge
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Bayesian methods	Machine learning
Année d'origine	1992	1970
Auteur d'origine	MacKay, D. J. C.	Hoerl, A.E. & Kennard, R.W.
Type	Probabilistic regularised regression	L2-regularized linear regression
Source fondatrice	MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	3	4
Résumé	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.	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.