Comparer des méthodes

	Régression linéaire simple	Régression Ridge
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	1805	1970
Auteur d'origine	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)	Hoerl, A.E. & Kennard, R.W.
Type	Parametric bivariate regression	L2-regularized linear regression
Source fondatrice	Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	SLR, ordinary least squares regression, OLS regression, bivariate regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	7	4
Résumé	Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.	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.