Salīdzināt metodes

	Bayesian Decision Tree	Regulārizēts lēmumu koks
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1998	1984
Autors	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tips	Bayesian ensemble / tree model	Supervised learning (regularized tree)
Pirmavots	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
Citi nosaukumi	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Saistītās	5	6
Kopsavilkums	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.