Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods

Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models. In urban sciences, the performance of these models depends heavily on hyperparameter tuning, yet systematic evaluations of tuning approaches remain limited. This study compares two traditional hyperparameter tuning methods, Random Search and Grid Search, with Optuna, a more recent and advanced optimization framework, using housing transaction data as an illustrative case. Our findings show that Optuna substantially outperforms the other methods, running 6.77 to 108.92 times faster while consistently achieving lower error values across multiple evaluation metrics. By demonstrating both efficiency and accuracy gains, this research underscores the potential of advanced tuning strategies to accelerate urban analytics and provide more reliable evidence for policy-making.