Comparer des méthodes

	Régression Ridge	Régression logistique
Domaine	Apprentissage automatique	Statistiques de recherche
Famille	Machine learning	Process / pipeline
Année d'origine	1970	1958
Auteur d'origine	Hoerl, A.E. & Kennard, R.W.	David Roxbee Cox
Type	L2-regularized linear regression	Method
Source fondatrice	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	logit model, binomial logistic regression, LR
Apparentées	4	3
Résumé	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.