Сравнение на методи

	Регресия Ласо	Регресия с гребен (Ridge Regression)
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1996	1970
Създател	Tibshirani, R.	Hoerl, A.E. & Kennard, R.W.
Тип	Regularized linear regression (L1 penalty)	L2-regularized linear regression
Основополагащ източник	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.
Други названия	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Свързани	4	4
Резюме	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.