Minimising changes to audit when updating decision trees

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 n...

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Veröffentlicht in:arXiv.org 2024-08
Hauptverfasser: Simmons, Anj, Barnett, Scott, Chaudhuri, Anupam, Singh, Sankhya, Sivasothy, Shangeetha
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Decision trees
Greedy algorithms
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Barnett, Scott
Chaudhuri, Anupam
Singh, Sankhya
Sivasothy, Shangeetha
description 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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subjects Decision trees
Greedy algorithms
title Minimising changes to audit when updating decision trees
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