A Bayesian Decision Tree Algorithm

2019/01/10 by Nuti, Giuseppe, Rugama, Lluís Antoni Jiménez, Cross, Andreea-Ingrid · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1901.03214

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.

Cited by

• A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology

Related

• Divide, Conquer, Combine Bayesian Decision Tree Sampling
• RJHMC-Tree for Exploration of the Bayesian Decision Tree Posterior
• Bayesian Decision Trees Inspired from Evolutionary Algorithms
• Decision Tree Induction Systems: A Bayesian Analysis
• Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars