Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shapley Values - #H2OWorld

This session was recorded in NYC on October 22nd, 2019.

Slides from the session can be viewed here: https://www.slideshare.net/secret/MBLzji959TgthN

Explainable Machine Learning with Shapley Values

Shapley values are popular approach for explaining predictions made by complex machine learning models. In this talk I will discuss what problems Shapley values solve, an intuitive presentation of what they mean, and examples of how they can be used through the ‘shap’ python package.

Bio: I am a senior researcher at Microsoft Research. Before joining Microsoft, I did my Ph.D. studies at the Paul G. Allen School of Computer Science & Engineering of the University of Washington working with Su-In Lee. My work focuses on explainable artificial intelligence and its application to problems in medicine and healthcare. This has led to the development of broadly applicable methods and tools for interpreting complex machine learning models that are now used in banking, logistics, sports, manufacturing, cloud services, economics, and many other areas.

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