Comparer des méthodes

	Forêt aléatoire bayésienne	Apprentissage semi-supervisé bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2015	2003–2006
Auteur d'origine	Taddy, M. et al.	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty
Type	Bayesian ensemble of decision trees	Probabilistic semi-supervised framework
Source fondatrice	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning
Apparentées	5	6
Résumé	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 semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.