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arXiv · 2408.16321

Minimising changes to audit when updating decision trees

Anj Simmons·Scott Barnett·Anupam Chaudhuri·Sankhya Singh·Shangeetha Sivasothy
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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.

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Anj Simmons, Scott Barnett, Anupam Chaudhuri, Sankhya Singh, Shangeetha Sivasothy. 2024-08-29. Minimising changes to audit when updating decision trees. https://arxiv.org/abs/2408.16321

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