# Decision Tree Induction Systems: A Bayesian Analysis

arXiv: https://arxiv.org/abs/1304.2732 · HTML: https://mlsanity.com/paper/1304.2732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

## Built on by (most cited first, 1 of 1)

- [Seer: Maximum Likelihood Regression for Learning-Speed Curves](https://mlsanity.com/paper/2610.02610/) (2026)

## Abstract (arXiv)

Decision tree induction systems are being used for knowledge acquisition in noisy domains. This paper develops a subjective Bayesian interpretation of the task tackled by these systems and the heuristic methods they use. It is argued that decision tree systems implicitly incorporate a prior belief that the simpler (in terms of decision tree complexity) of two hypotheses be preferred, all else being equal, and that they perform a greedy search of the space of decision rules to find one in which there is strong posterior belief. A number of improvements to these systems are then suggested.