مقایسهٔ روش‌ها

	درخت تصمیم بیزی (Bayesian Decision Tree)	درخت تصمیم منظم شده
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1998	1984
پدیدآور	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
نوع	Bayesian ensemble / tree model	Supervised learning (regularized tree)
منبع بنیادین	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., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
نام‌های دیگر	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
مرتبط	5	6
خلاصه	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.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.