Comparer des méthodes

	XGBoost	Arbre de décision	Régression logistique
Domaine	Apprentissage automatique	Apprentissage automatique	Statistiques de recherche
Famille	Machine learning	Machine learning	Process / pipeline
Année d'origine	2016	1984	1958
Auteur d'origine	Chen, T. & Guestrin, C.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox
Type	Ensemble (gradient-boosted decision trees)	Recursive partitioning (if-then rules)	Method
Source fondatrice	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	XGBoost, extreme gradient boosting, scalable tree boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR
Apparentées	5	5	3
Résumé	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.