Bayesian Optimization for Machine Learning : A Practical Guidebook

TL;DR

This paper provides a practical guide for machine learning practitioners on applying Bayesian optimization techniques to improve system engineering, demonstrating its benefits across common applications with open source tools.

Contribution

It offers a comprehensive, example-driven overview of Bayesian optimization tailored for machine learning practitioners, emphasizing practical implementation and benefits.

Findings

Bayesian optimization enhances machine learning system engineering.

Open source tools facilitate practical application of Bayesian methods.

The guide demonstrates improvements in common machine learning tasks.

Abstract

The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common machine learning applications.