Compara mètodes

	Impuls bayesià	XGBoost
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1999–2010	2016
Autor original	Ridgeway, G.; Chipman, H. A. et al.	Chen, T. & Guestrin, C.
Tipus	Probabilistic ensemble (Bayesian interpretation of boosting)	Ensemble (gradient-boosted decision trees)
Font seminal	Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	5	5
Resum	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.	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.