Compara mètodes

	Impuls bayesià	Gradient Boosting	Potenciament semisupervisat
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1999–2010	2001	1999–2009
Autor original	Ridgeway, G.; Chipman, H. A. et al.	Friedman, J. H.	Mallapragada, P. K.; Bennett, K. P.; and others
Tipus	Probabilistic ensemble (Bayesian interpretation of boosting)	Ensemble (sequential boosting of decision trees)	Semi-supervised ensemble method
Font seminal	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.	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.
Àlies	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Relacionats	5	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.	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.	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.