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Home > Linear Digressions > SHAP: Shapley Values in Machine Learning
Podcast: Linear Digressions
Episode:

SHAP: Shapley Values in Machine Learning
Category: Technology
Duration: 00:19:12
Publish Date: 2018-05-13 09:24:38
Description: Shapley values in machine learning are an interesting and useful enough innovation that we figured hey, why not do a two-parter? Our last episode focused on explaining what Shapley values are: they define a way of assigning credit for outcomes across several contributors, originally to understand how impactful different actors are in building coalitions (hence the game theory background) but now they're being cross-purposed for quantifying feature importance in machine learning models. This episode centers on the computational details that allow Shapley values to be approximated quickly, and a new package called SHAP that makes all this innovation accessible.
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