Comparer des méthodes

	Régression logistique (ML)	Régression linéaire (ML)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1958	1805–1809
Auteur d'origine	Cox, D. R.	Legendre, A.-M. & Gauss, C.F.
Type	Probabilistic linear classifier	Supervised regression
Source fondatrice	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
Alias	logit model, logit regression, binomial logistic regression, maximum entropy classifier	ordinary least squares regression, OLS, least squares regression, multiple linear regression
Apparentées	5	5
Résumé	Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.