Linganisha mbinu

	msitu wa Bayesian Random Forest	Kuimarisha	Uimarishaji wa Mteremko
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2015	1990–1997	2001
Mwanzilishi	Taddy, M. et al.	Schapire, R. E.; Freund, Y.	Friedman, J. H.
Aina	Bayesian ensemble of decision trees	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)
Chanzo asilia	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Majina mbadala	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Zinazohusiana	5	6	5
Muhtasari	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.