Comparer des méthodes

	Régression Lasso	Régression quantile
Domaine	Apprentissage automatique	Économétrie
Famille	Machine learning	Regression model
Année d'origine	1996	1978
Auteur d'origine	Tibshirani, R.	Koenker & Bassett
Type	Regularized linear regression (L1 penalty)	Conditional quantile regression
Source fondatrice	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	4	5
Résumé	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.