A Bayesian Decision Tree Algorithm

Noname manuscript No. (will be inserted by the editor) 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. KeywordsMachine learning · Bayesian statistics · Decision Trees · Random Forests 1 Introduction Decision trees are popular machine learning techniques applied to both classification andregression tasks.