Welcome to the WoodTapper documentation!¶

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

WoodTapper is supported by a peer reviewed publication:

WoodTapper: a Python package for explaining decision tree ensembles, Sakho et al. (2026) 📄

WoodTapper is a machine learning toolbox for investigating tree-based models. In this documentation you will find examples to be quickly getting started as well as some more in-depth example.

Installation¶

Getting started.

Tutorials¶

The mathematical formulation of WoodTapper modules are available here.

Example tutorials are also available for each module:

What's in there ?¶

Here is a quick overview of the different functionalities offered by WoodTapper. Further details are given in the rest of the documentation.

Rules Extractors¶

Example Explanation¶

📜 Citation¶

If you find the code useful, please consider citing us:

@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, consider citing:

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