Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models (forthcoming)

Kruschel S, Hambauer N, Weinzierl S, Zilker S, Kraus M, Zschech P (2024)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2024

Journal

Business & Information Systems Engineering Springer Verlag (Germany)

Abstract

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective.

Authors with CRIS profile

Sven Weinzierl Lehrstuhl für Digital Industrial Service Systems Sandra Zilker Lehrstuhl für Digital Industrial Service Systems Patrick Zschech

Involved external institutions

Universität Regensburg DE Germany (DE) Universität Leipzig DE Germany (DE)

How to cite

APA:

Kruschel, S., Hambauer, N., Weinzierl, S., Zilker, S., Kraus, M., & Zschech, P. (2024). Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models (forthcoming). Business & Information Systems Engineering.

MLA:

Kruschel, Sven, et al. "Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models (forthcoming)." Business & Information Systems Engineering (2024).

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