2986. Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Invited abstract in session TA-49: Fair and Interpretable Machine Learning, stream Analytics.
Tuesday, 8:30-10:00Room: Parkinson B10
Authors (first author is the speaker)
| 1. | Nico Hambauer
|
| Faculty of Informatics and Data Science, University of Regensburg | |
| 2. | Sven Kruschel
|
| University of Regensburg | |
| 3. | Sven Weinzierl
|
| Friedrich-Alexander-Universität Erlangen-Nürnberg | |
| 4. | Sandra Zilker
|
| Technische Hochschule Nürnberg Georg Simon Ohm | |
| 5. | Mathias Kraus
|
| University of Regensburg | |
| 6. | Patrick Zschech
|
| TU Dresden |
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.
Keywords
- Analytics and Data Science
- Decision Support Systems
- Machine Learning
Status: accepted
Back to the list of papers