Summary of On the Robustness Of Global Feature Effect Explanations, by Hubert Baniecki et al.
On the Robustness of Global Feature Effect Explanations
by Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper investigates the robustness of global post-hoc explanations for predictive models trained on tabular data, specifically focusing on partial dependence plots and accumulated local effects. The authors aim to provide theoretical bounds for evaluating the robustness of these methods against data and model perturbations. They explore the gap between the best-case scenario of accurate interpretation and the worst-case scenario of misinterpretation. By analyzing synthetic and real-world datasets, the study highlights the importance of considering the limitations of these interpretability techniques in applied sciences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well certain methods can explain why a machine learning model made a prediction. It’s like trying to figure out why a computer program gave a certain answer. The authors want to know how well these explanations hold up when the data or the model changes. They come up with some rules for testing how robust these explanations are and then test them on fake and real datasets. |
Keywords
» Artificial intelligence » Machine learning
