Minimising changes to audit when updating decision trees
Machine Learning
2024-08-30 v1
Abstract
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.
Cite
@article{arxiv.2408.16321,
title = {Minimising changes to audit when updating decision trees},
author = {Anj Simmons and Scott Barnett and Anupam Chaudhuri and Sankhya Singh and Shangeetha Sivasothy},
journal= {arXiv preprint arXiv:2408.16321},
year = {2024}
}
Comments
12 pages