Сравнение на методи

	Байесова LASSO регресия	Регресия Ласо
Област	Статистика	Машинно обучение
Семейство	Regression model	Machine learning
Година на възникване	2008	1996
Създател	Park & Casella	Tibshirani, R.
Тип	Bayesian regularized regression	Regularized linear regression (L1 penalty)
Основополагащ източник	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Други названия	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Свързани	5	4
Резюме	Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals.	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.