Why Use shapiq?¶
There are a couple of reasons why you might want to use shapiq:
Explanations with Shapley Interactions¶
shapiq directly extends on shap but also allows for computation of Shapley interactions.
These interactions can be used to explain models in more detail.
To facilitate any-order interactions, shapiq requires specific data structure and sets of algorithms.
Explanations with Shapley values¶
Similar to shap, shapiq can also be used to explain models with the well-established Shapley values.
Many algorithms that are available in shap are also available in shapiq.
Often, this is beneficial when you are looking into a higher-number of features.
Two Independent Perspectives: Explanation and Game Theory¶
shapiq offers two independent perspectives on the same problem: explanation and game theory.
We introduce the notion of a general game, which maps any machine learning problem (also outside the scope of machine learning) to a cooperative game without design decisions of explanation methods.
This allows for easy computation of many game-theoretic concepts, such as the Shapley value, Shapley interactions, or the Banzhaf value.
The explanation perspective is similar to shap and includes established mechanisms to transform any machine learning model into a cooperative game.
shapiq offers a unified interface to both perspectives.
Benchmarking of Novel Approaches¶
shapiq is a platform for benchmarking novel approaches in the field of Shapley values and Shapley interactions.
We implement many state-of-the-art algorithms and provide a unified interface to compare them.
Further, we provide a set of tools to evaluate the performance of these algorithms on pre-computed benchmarks tasks.