Comparer des méthodes

	Régression Lasso	Méthodologie des surfaces de réponse (RSM)
Domaine	Apprentissage automatique	Plans d'expériences
Famille	Machine learning	Hypothesis test
Année d'origine	1996	1951
Auteur d'origine	Tibshirani, R.	George E. P. Box & K. B. Wilson
Type	Regularized linear regression (L1 penalty)	Second-order polynomial response surface model
Source fondatrice	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	4	7
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.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.