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	Boosting bayesowski	Bayesian Random Forest	Wzmocnienie półnadzorowane
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1999–2010	2015	1999–2009
Twórca	Ridgeway, G.; Chipman, H. A. et al.	Taddy, M. et al.	Mallapragada, P. K.; Bennett, K. P.; and others
Typ	Probabilistic ensemble (Bayesian interpretation of boosting)	Bayesian ensemble of decision trees	Semi-supervised ensemble method
Źródło pierwotne	Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181.	Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976.	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.
Inne nazwy	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Pokrewne	5	5	5
Podsumowanie	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.	Bayesian Random Forest extends the classical random forest by placing a prior distribution over tree structures and leaf parameters, then sampling or approximating the posterior over that ensemble. The result is a set of predictions accompanied by calibrated uncertainty estimates — a capability standard random forests lack — making it valuable when knowing how confident the model is matters as much as the prediction itself.	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.