Skip to main content

A unified approach to explain the output of any machine learning model.

Project description

SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

shap-0.28.3.tar.gz (222.7 kB view hashes)

Uploaded Source

Built Distributions

shap-0.28.3-cp37-cp37m-win_amd64.whl (251.9 kB view hashes)

Uploaded CPython 3.7m Windows x86-64

shap-0.28.3-cp36-cp36m-win_amd64.whl (251.9 kB view hashes)

Uploaded CPython 3.6m Windows x86-64

shap-0.28.3-cp36-cp36m-macosx_10_7_x86_64.whl (274.5 kB view hashes)

Uploaded CPython 3.6m macOS 10.7+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page