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arXiv · 1901.03214

A Bayesian Decision Tree Algorithm

Giuseppe Nuti·Lluís Antoni Jiménez Rugama·Andreea-Ingrid Cross
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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.

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Giuseppe Nuti, Lluís Antoni Jiménez Rugama, Andreea-Ingrid Cross. 2019-01-10. A Bayesian Decision Tree Algorithm. https://arxiv.org/abs/1901.03214

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