[Q] What's the community's opinion of "interpretable ML/AI"?

• lime

  14 11,278 0.0 JavaScript

  Lime: Explaining the predictions of any machine learning classifier (by marcotcr)

LIME (https://github.com/marcotcr/lime) and Anchor (https://github.com/marcotcr/anchor), both by Marco Tulio Ribeiro (https://homes.cs.washington.edu/~marcotcr/).

• shap

  38 21,580 9.4 Jupyter Notebook

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

  

I've become a zealot about parametric stats, specifically from the Bayesian paradigm. Something about studying the core business problem, choosing the best distribution(s), and making inferences has been really rewarding for me. But increasingly, I'm seeing tools like SHAP, which allegedly enable users of black-box ML models to intuit what/how their models "think". (SHAP is just one example.)

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• anchor

  1 781 2.6 Jupyter Notebook

  Code for "High-Precision Model-Agnostic Explanations" paper (by marcotcr)

LIME (https://github.com/marcotcr/lime) and Anchor (https://github.com/marcotcr/anchor), both by Marco Tulio Ribeiro (https://homes.cs.washington.edu/~marcotcr/).

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