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 <https://github.com/shap/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 <https://arxiv.org/abs/2410.01649>`_:

.. code::

   @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
~~~~~~~~

.. toctree::
   :maxdepth: 1
   :caption: INTRODUCTION

   introduction/index
   introduction/installation
   introduction/start
   introduction/why-use-shapiq

.. toctree::
   :maxdepth: 2
   :caption: EXAMPLES & TUTORIALS

   auto_examples/index

.. toctree::
   :maxdepth: 2
   :caption: API REFERENCE

   api_reference

.. toctree::
   :maxdepth: 1
   :caption: BIBLIOGRAPHY

   related_software
   references