Obster, F, Ciolacu, MI and Humpe, A (2024) Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction. IEEE Access, 12. pp. 195613-195628. ISSN 2169-3536

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Abstract

This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ‘black-box’ models have grown. Theoretical assertions often propose a trade-off between model complexity (predictive performance) and transparency (interpretability); however, empirical evidence supporting this claim is limited and inconsistent. To address this gap, we introduce a novel interpretability scoring system - a Machine Learning Interpretability Rank-based scale - that combines objective measures such as the number of model parameters with subjective interpretability rankings across different model types. This comprehensive methodology includes stratified sampling, model tuning, and a two-step ranking system to operationalize this trade-off. Results reveal a significant negative correlation between interpretability and predictive performance for intrinsically interpretable models, reinforcing the notion of a trade-off. However, this relationship does not hold for black-box models, suggesting that for these algorithms, predictive performance can be prioritized over interpretability. This study contributes to the ongoing discourse on explainable AI, providing practical insights and tools to help researchers and practitioners achieve a balance between model complexity and transparency. We recommend to prioritise more interpretable models when predictive performance is comparable. Our scale provides a transparent and efficient framework for implementing this heuristic and improving parameter optimization. Further research should extend this analysis to unstructured data, explore different interpretability methods, and develop new metrics for evaluating the trade-off across diverse contexts.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 4611 Machine Learning; Bioengineering; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Generic health relevance; 08 Information and Computing Sciences; 09 Engineering; 10 Technology; 40 Engineering; 46 Information and computing sciences
Subjects:	Q Science > QB Astronomy Q Science > QC Physics
Divisions:	Astrophysics Research Institute
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor:	A Symplectic
Date Deposited:	11 Apr 2025 15:10
Last Modified:	11 Apr 2025 15:10
DOI or ID number:	10.1109/access.2024.3521242
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26174

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