Comparer des méthodes

	Arbre de décision bayésien	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1998	2001
Auteur d'origine	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Breiman, L.
Type	Bayesian ensemble / tree model	Ensemble (bagging of decision trees)
Source fondatrice	Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
Résumé	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.	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.