Version 1
: Received: 4 June 2024 / Approved: 5 June 2024 / Online: 5 June 2024 (10:59:28 CEST)
Version 2
: Received: 26 July 2024 / Approved: 29 July 2024 / Online: 29 July 2024 (08:46:31 CEST)
How to cite:
Kee, T.; Ho, W. K. Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods. Preprints2024, 2024060264. https://doi.org/10.20944/preprints202406.0264.v2
Kee, T.; Ho, W. K. Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods. Preprints 2024, 2024060264. https://doi.org/10.20944/preprints202406.0264.v2
Kee, T.; Ho, W. K. Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods. Preprints2024, 2024060264. https://doi.org/10.20944/preprints202406.0264.v2
APA Style
Kee, T., & Ho, W. K. (2024). Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods. Preprints. https://doi.org/10.20944/preprints202406.0264.v2
Chicago/Turabian Style
Kee, T. and Winky K.O. Ho. 2024 "Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods" Preprints. https://doi.org/10.20944/preprints202406.0264.v2
Abstract
The advancement of urban scholarship and the effective addressing of urban environment challenges necessitate the adoption of sophisticated analytical methods. Urban scholars and policymakers need advanced analytical methods to tackle issues like gentrification, housing affordability, and urban sprawl. Predictive models are crucial in the realm of urban sciences, and hyperparameter tuning methods can significantly improve their accuracy and efficiency. Our study compares three such methods — Optuna, Random Search, and Grid Search — using a housing transaction dataset. We find that Optuna is not only 5.58 to 70.50 times faster than the other two methods when applied to Random Forest and Gradient Boosting Machine algorithms, but also achieves lower error values in key evaluation metrics on the test set, such as mean absolute error, mean squared error, mean absolute percentage error and root mean squared error.
Keywords
hyperparameter tuning; optuna; grid search; random search; urban studies
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
