Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/338611 
Year of Publication: 
2025
Citation: 
[Journal:] Business & Information Systems Engineering [ISSN:] 1867-0202 [Volume:] 68 [Issue:] 1 [Publisher:] Springer Fachmedien Wiesbaden [Place:] Wiesbaden [Year:] 2025 [Pages:] 159-183
Publisher: 
Springer Fachmedien Wiesbaden, Wiesbaden
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, the 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.
Subjects: 
Decision support
Predictive analytics
Interpretable machine learning
Generalized additive models
Explainable artificial intelligence
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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