Linganisha mbinu

	Regression ya Mfumo wa Bayesian (Bayesian Ridge Regression)	Lasso Regression	Regressioni ya Mtepe
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Bayesian methods	Machine learning	Machine learning
Mwaka wa asili	1992	1996	1970
Mwanzilishi	MacKay, D. J. C.	Tibshirani, R.	Hoerl, A.E. & Kennard, R.W.
Aina	Probabilistic regularised regression	Regularized linear regression (L1 penalty)	L2-regularized linear regression
Chanzo asilia	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Majina mbadala	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Zinazohusiana	3	4	4
Muhtasari	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.	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.