# Minimising changes to audit when updating decision trees

arXiv: https://arxiv.org/abs/2408.16321 · HTML: https://mlsanity.com/paper/2408.16321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [Born-Again Tree Ensembles](https://mlsanity.com/paper/2003.11132/) (2020)
- [Interpreting Tree Ensembles with inTrees](https://mlsanity.com/paper/1408.5456/) (2014)
- [Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach](https://mlsanity.com/paper/1606.09066/) (2016)
- [Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach](https://mlsanity.com/paper/2206.03718/) (2022)
- [Diverse Rule Sets](https://mlsanity.com/paper/2006.09890/) (2020)

## Abstract (arXiv)

Interpretable models are important, but what happens when the model is updated on new training data? We propose an algorithm for updating a decision tree while minimising the number of changes to the tree that a human would need to audit. We achieve this via a greedy approach that incorporates the number of changes to the tree as part of the objective function. We compare our algorithm to existing methods and show that it sits in a sweet spot between final accuracy and number of changes to audit.