Algorithms for Interpretable Machine Learning

Reception to follow talkAbstract:It is extremely important 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. I will describe several approaches, including an algorithm based on discrete optimization, and an algorithm based on Bayesian analysis.Collaborators are: Ben Letham, Allison Chang, Tyler McCormick, David Madigan, Shawn Qian