WoodTapper Documentation¶

User-friendly and scalable Python package for tapping decision tree ensembles

Overview¶

WoodTapper is a comprehensive Python package designed for interpretability and explainability of decision tree ensembles. It provides tools for extracting interpretable rules and generating example-based explanations from tree-based models.

The package is supported by peer-reviewed research:

Sakho et al. (2026). WoodTapper: a Python package for explaining decision tree ensembles. Journal of Open Source Software, 11(121), 10112. 📄 https://doi.org/10.21105/joss.10112

Quick Start¶

Modules¶

Rules Extraction¶

Extract interpretable decision rules from tree-based models:

Tutorials: Rules Extraction

Example-Based Explainability¶

Generate instance-level explanations:

Tutorials: Example-Based Explainability

License¶

WoodTapper is distributed under the MIT License.

This permissive license allows you to use, copy, modify, merge, publish, distribute, sublicense, and sell copies of the software, provided that the copyright notice and permission notice are included in all copies or substantial portions of the software.

Acknowledgements¶

WoodTapper has been developed through a collaboration between the Artefact Research Center and the Laboratoire de Probabilités Statistiques et Modélisation (LPSM) of Sorbonne University.

Citation¶

If you use WoodTapper in your research, please cite:

@article{Sakho2026,
  doi = {10.21105/joss.10112},
  url = {https://doi.org/10.21105/joss.10112},
  year = {2026},
  publisher = {The Open Journal},
  volume = {11},
  number = {121},
  pages = {10112},
  author = {Sakho, Abdoulaye and Aouad, Jad and Gauthier, Carl-Erik and Malherbe, Emmanuel and Scornet, Erwan},
  title = {WoodTapper: a Python package for explaining decision tree ensembles},
  journal = {Journal of Open Source Software}
}

For SIRUS methodology:

@article{benard2021sirus,
  title={SIRUS: Stable and interpretable rule set for classification},
  author={Benard, Clement and Biau, Gerard and Da Veiga, Sebastien and Scornet, Erwan},
  journal = {Machine Learning},
  year = {2021}
}