Optimizing Machine Learning Models

In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications. We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine. We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.