Σύγκριση μεθόδων

	Μπεϋζιανή Παλινδρόμηση Ridge	Παλινδρόμηση Lasso
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Bayesian methods	Machine learning
Έτος προέλευσης	1992	1996
Δημιουργός	MacKay, D. J. C.	Tibshirani, R.
Τύπος	Probabilistic regularised regression	Regularized linear regression (L1 penalty)
Θεμελιώδης πηγή	MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Εναλλακτικές ονομασίες	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Συναφείς	3	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.	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.