Abstract Details
Activity Number:
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328
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Type:
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Invited
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #307101 |
Title:
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Big Data Meets Human Understanding : Interpretability in Predictive Modeling with the Bayesian List Machine
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Author(s):
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Benjamin Letham and Cynthia Rudin and Tyler H. McCormick and David Madigan*+
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Companies:
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MIT and Massachusetts Institute of Technology and University of Washington, Seattle and Columbia University
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Keywords:
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power grid ;
machine learning ;
maintenance ;
supervised ranking
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Abstract:
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Interpretability is an important quality in a predictive model. In this talk, I will describe how we are are designing models consisting of a series of if...then... rules that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. Specifically, I will introduce the "Bayesian List Machine," and discuss its application to big data in medicine. In particular, I will provide an alternative to the CHADS2 score, which is a widely used scoring system for stroke prediction.
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Authors who are presenting talks have a * after their name.
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