Porovnat metody

	Bayesovský náhodný les	Polosupervizované posilování (Semi-supervised Boosting)
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2015	1999–2009
Tvůrce	Taddy, M. et al.	Mallapragada, P. K.; Bennett, K. P.; and others
Typ	Bayesian ensemble of decision trees	Semi-supervised ensemble method
Původní zdroj	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.
Další názvy	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Příbuzné	5	5
Shrnutí	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.