Published April 10, 2024
| Version
1.0
Python Library
Shap
Owner
Description
SHAP is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions
Shap is integrated in the Kaa Explainability Platform.
Documentation
User Manual
User manual is available online, here.
Methodological Guidelines
Scientific Contribution
Benchmark
Application over use-case
State of the Art
Demonstrator
A restricted demonstrator is available here. It relies on visual inspection.
Support
Support for Shap must be obtained directly from the component owners, you can contact them directly here.
Additional details
- Documentation Link https://github.com/slundberg/shap
- Offical Website https://github.com/slundberg/shap
- Download Link pip install Shap
- Technological maturity
Engineering roles
Human Cognition Engineer
Embedded Software Engineer
ML-Algorithm Engineer
Data Engineer
Engineering activities
Functional Set
Explainability