Case Study: Automatically Training a Classifier with OptiML

This blog post, the second in a series of 6 posts exploring OptiML, the new feature for automatic model optimization on BigML, focuses on a real-world use case within the healthcare industry: medical appointment "no shows". We will demonstrate how OptiML uses Bayesian parameter optimization to search for the the best performing model for your data. The status of the search is continually updated in the BigML Dashboard and the process yields a list of models ranked by performance, which enables further exploration, evaluation, and prediction tasks. With regards to healthcare expenses, "no show" appointments represent a major expense, estimated to cost hospitals over $150 billion per year. A "no show" is when all the necessary information for a medical appointment has been delivered, yet the patient fails to arrive at the scheduled appointment.