Σύγκριση μεθόδων

	Μπεϋζιανή Παλινδρόμηση Ridge	Elastic Net	Παλινδρόμηση Ridge
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Bayesian methods	Machine learning	Machine learning
Έτος προέλευσης	1992	2005	1970
Δημιουργός	MacKay, D. J. C.	Zou, H. & Hastie, T.	Hoerl, A.E. & Kennard, R.W.
Τύπος	Probabilistic regularised regression	Regularized linear regression (L1 + L2 penalty)	L2-regularized linear regression
Θεμελιώδης πηγή	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Εναλλακτικές ονομασίες	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	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Συναφείς	3	4	4
Σύνοψη	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.	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.