Comparar métodos

	Árvore de Decisão Bayesiana	Árvore de Decisão Regularizada
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1998	1984
Autor original	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tipo	Bayesian ensemble / tree model	Supervised learning (regularized tree)
Fonte seminal	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
Outros nomes	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Relacionados	5	6
Resumo	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.