SHAP: Shapley Values in Machine Learning

Linear Digressions

• Episode: SHAP: Shapley Values in Machine Learning
• Website: Linear Digressions
• Feed URL: http://feeds.feedburner.com/udacity-linear-digressions?format=xml
• Duration: 00:19:12
• Published: 2018-05-13 16:24

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.