Comparer des méthodes

	Gradient Boosting	Régression logistique
Domaine	Apprentissage automatique	Statistiques de recherche
Famille	Machine learning	Process / pipeline
Année d'origine	2001	1958
Auteur d'origine	Friedman, J. H.	David Roxbee Cox
Type	Ensemble (sequential boosting of decision trees)	Method
Source fondatrice	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	logit model, binomial logistic regression, LR
Apparentées	5	3
Résumé	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.