A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees

Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis, Mehdi Sabiri, Nicolas Szczepanski
10/21/2025
cs.LGcs.AI

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Abstract

We present a new approach for distilling boosted trees into decision trees, in the objective of generating an ML model offering an acceptable compromise in terms of predictive performance and interpretability. We explain how the correction approach called rectification can be used to implement such a distillation process. We show empirically that this approach provides interesting results, in comparison with an approach to distillation achieved by retraining the model.

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Cite this paper

@article{audemard2025rectificationbased,
  title  = {A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees},
  author = {Gilles Audemard and Sylvie Coste-Marquis and Pierre Marquis and Mehdi Sabiri and Nicolas Szczepanski},
  year   = {2025},
  eprint = {2510.18615},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.18615v1}
}

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