Compara mètodes

	Regressió Lasso	Regressió Ridge	Màquina de Vectors de Suport (Classificació)
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1996	1970	1995
Autor original	Tibshirani, R.	Hoerl, A.E. & Kennard, R.W.	Cortes, C. & Vapnik, V.
Tipus	Regularized linear regression (L1 penalty)	L2-regularized linear regression	Maximum-margin classifier (kernel method)
Font seminal	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Àlies	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionats	4	4	5
Resum	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.