The shapiq Python package

Shapley Interaction Quantification (shapiq) is a Python package for (1) approximating any-order Shapley interactions, (2) benchmarking game-theoretical algorithms for machine learning, (3) explaining feature interactions of model predictions. shapiq extends the well-known shap package for both researchers working on game theory in machine learning, as well as the end-users explaining models. SHAP-IQ extends individual Shapley values by quantifying the synergy effect between entities (aka players in the jargon of game theory) like explanatory features, data points, or weak learners in ensemble models. Synergies between players give a more comprehensive view of machine learning models.

If you enjoy using the shapiq package, please consider citing our NeurIPS paper:

@inproceedings{muschalik2024shapiq,
  title     = {shapiq: Shapley Interactions for Machine Learning},
  author    = {Maximilian Muschalik and Hubert Baniecki and Fabian Fumagalli and
               Patrick Kolpaczki and Barbara Hammer and Eyke H\"{u}llermeier},
  booktitle = {The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year      = {2024},
  url       = {https://openreview.net/forum?id=knxGmi6SJi}
}

Contents

INTRODUCTION

EXAMPLES & TUTORIALS

API REFERENCE

BIBLIOGRAPHY