Computing That Serves

Informing the use of Hyper-parameter Optimization through Meta-learning

Samantha Sanders MS Thesis Defense

Friday, May 19th, 8:00 am

3350 TMCB – Conference Room

Advisor: Christophe Giraud-Carrier



One of the challenges of data mining is finding hyper-parameters for a learning algorithm that will produce the best model for a given dataset. Hyper-parameter optimization automates this process, but it can still take significant time. It has been found that hyper-parameter optimization does not always result in induced models with significant improvement over default hyper-parameters, yet no systematic analysis of the role of hyper-parameter optimization in machine learning has been conducted. We propose the use of meta-learning to inform the decision to optimize hyper-parameters based on whether default hyper-parameter performance can be surpassed in a given amount of time. We will build a base of meta-knowledge, through a series of experiments, to build predictive models that will assist in the decision process.