A Bayesian Decision Tree Algorithm
This work addresses efficiency issues in Bayesian Decision Trees for researchers and practitioners in machine learning, though it appears incremental as it builds on existing methods without major breakthroughs.
The paper tackles the computational cost of Bayesian Decision Trees by introducing a general algorithm that avoids MCMC and pruning, and finds that a greedy-modal tree performs similarly to Random Forests in numerical examples.
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.