Summary of Integrating White and Black Box Techniques For Interpretable Machine Learning, by Eric M. Vernon et al.
Integrating White and Black Box Techniques for Interpretable Machine Learning
by Eric M. Vernon, Naoki Masuyama, Yusuke Nojima
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed ensemble classifier design addresses the trade-off between interpretability and performance in machine learning algorithm design. The authors present an approach that leverages the strengths of both highly-interpretable classifiers (white box models) and more powerful, but less interpretable classifiers (black box models). By combining these two types of models, the proposed design can achieve better performance on difficult tasks while still maintaining interpretability for easier inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to build machine learning models that are both good at solving problems and easy for humans to understand. This is important because many current models are very powerful but don’t work in a way that’s easy for people to understand. The authors suggest using two types of models: one that’s simple and easy to understand, and another that’s more complex and better at getting the right answer. By combining these two, you can get a model that does well on hard tasks and is still understandable. |
Keywords
» Artificial intelligence » Machine learning
