Questions Tagged [shap]

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 (see the SHAP NIPS paper for details).

Question is empty. Ask new Question

Popular Questions

Top Members

Trending Tags

redux version-control ide rss line azure-ad-b2c bundler html-agility-pack abap orientdb statusbar implementation projection fwrite intentfilter one-to-one lua-table nonetype dotnetopenauth rounded-corners timezone-offset moss rmagick interstitial resourcebundle html-input drawimage recharts clip-path probability-density udf unobtrusive-javascript javaagents python-behave rollover discord-jda postman-collection-runner azure-queues mysql-real-escape-string desire2learn appveyor accounting azure-data-flow filepond twincat websphere-6.1 imbalanced-data range-v3 pygments webhdfs