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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16321 |
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| _version_ | 1866917762218065920 |
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| author | Simmons, Anj Barnett, Scott Chaudhuri, Anupam Singh, Sankhya Sivasothy, Shangeetha |
| author_facet | Simmons, Anj Barnett, Scott Chaudhuri, Anupam Singh, Sankhya Sivasothy, Shangeetha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Minimising changes to audit when updating decision trees Simmons, Anj Barnett, Scott Chaudhuri, Anupam Singh, Sankhya Sivasothy, Shangeetha Machine Learning 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. |
| title | Minimising changes to audit when updating decision trees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.16321 |