It is essential in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.I will discuss recent work on interpretable predictive modeling with decision lists. In particular, I will describe several approaches for constructing lists, including a discrete optimization approach and a Bayesian analysis approach. I will provide applications of this work to personalized medicine, energy grid reliability, and predictive policing.
Collaborators are: Ben Letham, Tyler McCormick, David Madigan, Allison Chang and Shawn Qian
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