পদ্ধতির তুলনা করুন

	বেয়েশীয় ডিসিশন ট্রি	নিয়ন্ত্রিত সিদ্ধান্ত বৃক্ষ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1998	1984
প্রবর্তক	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
ধরন	Bayesian ensemble / tree model	Supervised learning (regularized tree)
মৌলিক উৎস	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
অপর নাম	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
সম্পর্কিত	5	6
সারসংক্ষেপ	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.