विधियों की तुलना करें

	बायेसियन मल्टीपल लीनियर रिग्रेशन	लासो रिग्रेशन
क्षेत्र	सांख्यिकी	मशीन अधिगम
परिवार	Regression model	Machine learning
उद्भव वर्ष	1971	1996
प्रवर्तक	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Tibshirani, R.
प्रकार	Bayesian parametric regression	Regularized linear regression (L1 penalty)
मौलिक स्रोत	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
उपनाम	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
संबंधित	6	4
सारांश	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	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.