Minimising changes to audit when updating decision trees
This addresses the challenge of maintaining interpretability in models for users who need to audit updates, though it is incremental.
The paper tackles the problem of updating decision trees on new training data while minimizing the number of changes that require human audit, achieving a balance between final accuracy and audit changes.
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.