The role of hyperparameters in machine learning models and how to tune them
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Date
2024-02-05
Authors
Neunhoeffer, Marcel
Version
Published version
OA Version
Citation
Arnold C, Biedebach L, Küpfer A, Neunhoeffer M. The role of hyperparameters in machine learning models and how to tune them. Political Science Research and Methods. Published online 2024:1-8. doi:10.1017/psrm.2023.61
Abstract
Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.
Description
License
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of EPS Academic Ltd. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. This article has been published under a Read & Publish Transformative Open Access (OA) Agreement with CUP.