March 28, 2024, 4:41 a.m. | Jodie A. Cochrane, Adrian Wills, Sarah J. Johnson

cs.LG updates on

arXiv:2403.18147v1 Announce Type: new
Abstract: Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging because these approaches need to explore both the tree structure space and the space of decision parameters associated with each tree structure. This has been handled by using Markov Chain Monte Carlo (MCMC) methods, where a Markov Chain is constructed to …

abstract arxiv bayesian bayesian inference cs.lg decision decision trees explore flexibility inference interpretability paper predictions predictive predictive models sampling space tree trees type uncertainty

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