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	Bayesian Random Forest	Bayesian Decision Tree
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2015	1998
Twórca	Taddy, M. et al.	Chipman, H. A.; George, E. I.; McCulloch, R. E.
Typ	Bayesian ensemble of decision trees	Bayesian ensemble / tree model
Źródło pierwotne	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.	Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.
Inne nazwy	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree
Pokrewne	5	5
Podsumowanie	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.	Bayesian Decision Tree (Bayesian CART) places a prior distribution over tree structures and leaf parameters, then uses Markov chain Monte Carlo to explore the posterior distribution of trees given data. Instead of a single best tree, it produces a distribution of plausible trees whose predictions are averaged, yielding calibrated uncertainty estimates alongside point predictions.