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	बयेसियन बूस्टिंग	बायेसियन रैंडम फ़ॉरेस्ट	बूस्टिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1999–2010	2015	1990–1997
प्रवर्तक	Ridgeway, G.; Chipman, H. A. et al.	Taddy, M. et al.	Schapire, R. E.; Freund, Y.
प्रकार	Probabilistic ensemble (Bayesian interpretation of boosting)	Bayesian ensemble of decision trees	Sequential ensemble (iterative reweighting)
मौलिक स्रोत	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.	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.
उपनाम	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
संबंधित	5	5	6
सारांश	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.	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.