Sammenlign metoder

	Bayesian Bagging	Bayesiansk Boosting	Random Forest
Fagområde	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	2001	1999–2010	2001
Ophavsperson	Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981)	Ridgeway, G.; Chipman, H. A. et al.	Breiman, L.
Type	Ensemble (Bayesian bootstrap aggregation)	Probabilistic ensemble (Bayesian interpretation of boosting)	Ensemble (bagging of decision trees)
Oprindelig kilde	Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001).	Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasser	Bayesian bootstrap aggregation, BB-ensemble, Bayesian model averaging via bootstrap, Bayesian bagged ensemble	Bayesian ensemble boosting, probabilistic boosting, Bayesian additive model, Bayesian boosted ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relaterede	6	5	4
Resumé	Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.