Summary of Challenging the Performance-interpretability Trade-off: An Evaluation Of Interpretable Machine Learning Models, by Sven Kruschel et al.
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
by Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning is increasingly being applied across various domains to inform data-driven decision-making. This study explores the performance of seven generalized additive models (GAMs) compared to seven traditional machine learning models on twenty tabular benchmark datasets. An extensive hyperparameter search and cross-validation were conducted, resulting in 68,500 model runs. Additionally, the study examines the visual output of the models to assess their interpretability. The results show that GAMs can achieve high accuracy without sacrificing interpretability, contradicting the assumption that only black-box models are effective. This paper highlights the potential of GAMs as powerful interpretable models in information systems and derives implications for future work from a socio-technical perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping us make better decisions by using data to predict what might happen. Some models are super smart but hard to understand, while others are easy to understand but not as good at predicting things. This study looked at special models called generalized additive models (GAMs) and compared them to other popular models on lots of different datasets. They tested many different settings for each model and then looked at how well they worked together with how easily we could understand why they made certain predictions. The results show that GAMs can be super good at predicting things without being too complicated, which is important because it means we can use them to make informed decisions. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning
