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On Hyperparameter Optimization of Machine Learning Algorithms:Theory and Practice - 2020

On Hyperparameter Optimization Of Machine Learning Algorithms:Theory And Practice

Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Li Yang, Abdallah Shami

Journal name:  Neurocomputing

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.neucom.2020.07.061

Volume Information:  Volume 415, 20 November 2020, Pages 295-316