2025
doi: 10.12720/jait.16.9.1329-1337
Energy Distance Based Similarity Analysis of Hyperparameter Optimization Results for Random Forests
Thuy Thi Tran1,2
,
Nghia Quoc Phan1 ,
Hiep Xuan Huynh3,4,*
- 1Assessment Office, Tra Vinh University, Tra Vinh, Vietnam
- 2Network Management Center, University of Cuu Long, Vinh Long, Vietnam
- 3College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam
- 4CTU Leading Research Team on Automation, Artificial Intelligence, inforMation tEchnology and Digital Transformation, Can Tho University, Can Tho, Vietnam
- Manuscript receivedJanuary 24, 2025
- revisedFebruary 27, 2025
- acceptedMay 9, 2025
- publishedSeptember 19, 2025
Abstract
In this study, we propose a novel approach for analyzing the results of Hyperparameter Optimization (HPO) for Random Forest (RF) models by applying Energy Distance (ED), a metric based on pairwise Euclidean distances. This method provides a quantitative measure of the similarities and differences between the configurations of hyperparameters and their corresponding performance metrics. We use a dataset from a hyperparameter optimization experiment for RF, where we explore the relationship between hyperparameter settings and model accuracy. The results indicate that Energy Distance can offer useful insights into the proximity of different hyperparameter configurations and help identify clusters of similar configurations, which can be useful for model selection and optimization.
Keywords
- energy distance
- hyperparameter optimization
- random forest
- Euclidean distance
- machine learning
How to Cite
Thuy Thi Tran, Nghia Quoc Phan, and Hiep Xuan Huynh, "Energy Distance Based Similarity Analysis of Hyperparameter Optimization Results for Random Forests," Journal of Advances in Information Technology, vol. 16, no. 9, pp. 1329-1337, 2025. https://doi.org/10.12720/jait.16.9.1329-1337
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
