A Bayesian Decision Tree Algorithm
Machine Learning
2020-09-23 v3 Machine Learning
Abstract
Bayesian Decision Trees are known for their probabilistic interpretability. However, their construction can sometimes be costly. In this article we present a general Bayesian Decision Tree algorithm applicable to both regression and classification problems. The algorithm does not apply Markov Chain Monte Carlo and does not require a pruning step. While it is possible to construct a weighted probability tree space we find that one particular tree, the greedy-modal tree (GMT), explains most of the information contained in the numerical examples. This approach seems to perform similarly to Random Forests.
Cite
@article{arxiv.1901.03214,
title = {A Bayesian Decision Tree Algorithm},
author = {Giuseppe Nuti and Lluís Antoni Jiménez Rugama and Andreea-Ingrid Cross},
journal= {arXiv preprint arXiv:1901.03214},
year = {2020}
}
Comments
15 pages, 5 figures