On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

TL;DR

This paper reviews hyper-parameter optimization techniques for machine learning, discussing their applications, comparing their performance on benchmark datasets, and highlighting open challenges to guide practitioners and researchers.

Contribution

It introduces state-of-the-art optimization methods, provides practical frameworks, and offers a comprehensive survey of hyper-parameter tuning challenges and solutions.

Findings

Different optimization techniques have varying strengths and drawbacks.

Benchmark experiments compare the performance of multiple hyper-parameter optimization methods.

Open challenges in hyper-parameter optimization are identified and discussed.

Abstract

Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. It often requires deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization. This survey paper will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.