Comparer des méthodes

	Régression quantile	Régression Lasso
Domaine	Économétrie	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	1978	1996
Auteur d'origine	Koenker & Bassett	Tibshirani, R.
Type	Conditional quantile regression	Regularized linear regression (L1 penalty)
Source fondatrice	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	conditional quantile regression, regression quantiles, Kantil Regresyon	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	5	4
Résumé	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.	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.