The critical role of hyperparameter tuning in machine learning: Implications for reproducibility and model comparison
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Date
2025-12-31
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Kwara State University
Abstract
Despite being a fundamental aspect of machine learning model development, hyperparameter tuning remains
underreported in the literature. This article highlights the importance of hyperparameter optimisation, outlines common
hyperparameters across various algorithms, and discusses the consequences of inadequate hyperparameter documentation. We
argue that the lack of transparency in hyperparameter settings impedes reproducibility, hinders fair model comparisons, and
contributes to the hyperparameter deception. The importance of hyperparameter tuning in machine learning was demonstrated
by comparing the performance of the decision tree, support vector machine and random forest models on Iris, Digits and Breast
Cancer datasets using default and tuned hyperparameters. This further justifies the need to document and report the process and
values of the hyperparameter settings used in the models. To facilitate this, an architecture that encourages the documentation
of the hyperparameters has been proposed. By emphasising the need for comprehensive reporting, this study aims to raise
awareness and encourage best practices in machine learning research
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Citation
15. Rafiu Mope Isiaka, Shakirat Ronke Yusuff, Akinbowale Nathaniel Babatunde, Shuaib Mohammed (2025). The critical role of hyperparameter tuning in machine learning: implications for reproducibility and model comparison. Technoscience Journal for Community Development in Africa. Vol 4 (2025) 227-240. Published by Kwara State University, Malete. 227-240. https://doi.org/10.5281/zenodo.18108973