Abstract: Interpretability is an important factor for models to be used and trusted in many applications.  Disjunctive normal forms, also known as or-of-and models, are models with classification rules of the form "Predict True if (A and B) or (A and C) or D."  They are an appealing form of classifier because one can easily trace how a classification decision was made, and has some basis in human decision-making.  In this talk, I will talk about a Bayesian approach to learning or-of-and models and describe an application to context-aware recommender systems.  

This is joint work with Tong Wang and Cynthia Rudin