Comparer des méthodes

	Boosting Bayésien	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999–2010	2001
Auteur d'origine	Ridgeway, G.; Chipman, H. A. et al.	Friedman, J. H.
Type	Probabilistic ensemble (Bayesian interpretation of boosting)	Ensemble (sequential boosting of decision trees)
Source fondatrice	Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	5	5
Résumé	Bayesian boosting integrates probabilistic Bayesian inference with boosting ensemble techniques, combining multiple weak learners while maintaining full uncertainty quantification over predictions. Unlike standard gradient boosting that produces a single point estimate, Bayesian boosting yields a posterior distribution over the ensemble output, enabling calibrated confidence intervals alongside predictions.	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.