Confronta i metodi

	Regressione con Vettori di Supporto	Regressione Lasso	Regressione Ridge
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2004	1996	1970
Ideatore	Smola, A.J. & Schölkopf, B.	Tibshirani, R.	Hoerl, A.E. & Kennard, R.W.
Tipo	Kernel-based supervised model (epsilon-insensitive regression)	Regularized linear regression (L1 penalty)	L2-regularized linear regression
Fonte seminale	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	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.
Alias	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Correlati	4	4	4
Sintesi	Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.	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.