Interpreting models using SHapley Additive exPlantions (SHAP) with Shiu-Tang Li

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Recursion Pharmaceuticals

41 400 W · Salt Lake City, UT

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Please enter from 400W, across from the Vivint Center.

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Data scientists often struggle between model accuracy and model interpretability. To make models more interpretable, sometimes tree-based models have to be replaced with much simpler models like logistic regression. There is a growing need to better explain more complicated models. SHAP is now one of the best tools for this task; both XGBoost and LightGBM have incorporated SHAP into their library, and it’s now available in the most recent package.

I will talk about the math behind SHAP, comparison of SHAP with other feature importance algorithms, and a few code examples.