Rich Caruana: Intelligible Machine Learning Models for HealthCare

In machine learning often a tradeoff must be made between accuracy and intelligibility. This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as health care. We have developed a learning method based on generalized additive models (GAMs) that is often as accurate as full complexity models, but remains as intelligible as linear/logistic regression models. In the talk I'll present two case studies where these high-performance generalized additive models (GA2Ms) are applied to healthcare problems yielding intelligible models with state-of-the-art accuracy: a pneumonia risk prediction case studyand a 30-day hospital readmission case study.