On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

• URL: http://arxiv.org/abs/2007.15745v3
• Date: Wed, 5 Oct 2022 03:06:52 GMT
• Title: On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
• Authors: Li Yang, Abdallah Shami
• Abstract summary: We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. This paper will help industrial users, data analysts, and researchers to better develop machine learning models.
• Score: 10.350337750192997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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