What Are the Most Important Statistical Ideas of the Past 50 Years?

• shap

  38 21,580 9.4 Jupyter Notebook

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

  

Seconding Chris Molnar's excellent writeup. I also find the readme & example notebooks in Scott Lundberg's github repo to be a great way to get started. There are also references there for the original papers, which are surprisingly readable, imo. https://github.com/slundberg/shap

• interpret

  6 5,988 9.7 C++

  Fit interpretable models. Explain blackbox machine learning.

  

You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret

They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)

Disclosure: I helped build this open source package

• 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.

  

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