Salīdzināt metodes

	Bayesian Decision Tree	Beiziešu nejaušais mežs
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1998	2015
Autors	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Taddy, M. et al.
Tips	Bayesian ensemble / tree model	Bayesian ensemble of decision trees
Pirmavots	Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.	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.
Citi nosaukumi	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest
Saistītās	5	5
Kopsavilkums	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.	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.