Minimising changes to audit when updating decision trees
<|reference_start|>Minimising changes to audit when updating decision trees: 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.<|reference_end|>
@article{simmons2024minimising,
    title={Minimising changes to audit when updating decision trees},
    author={Anj Simmons, Scott Barnett, Anupam Chaudhuri, Sankhya Singh,
  Shangeetha Sivasothy},
    journal={arXiv preprint arXiv:2408.16321},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16321},
    primaryClass={cs.LG}
}