Summary of Model-agnostic Interpretation Framework in Machine Learning: a Comparative Study in Nba Sports, by Shun Liu
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
by Shun Liu
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 proposes an innovative framework to reconcile the trade-off between model performance and interpretability in deep learning models. The framework is designed to enable end-to-end processing while preserving interpretability by fusing diverse interpretability techniques and modularized data processing. By doing so, it sheds light on the decision-making processes of complex models without compromising their performance. The approach has been extensively tested and validated for its superior efficacy in achieving a harmonious balance between computational efficiency and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make deep learning models more understandable. Right now, these models are like black boxes that don’t explain why they made certain decisions. This can be a problem when using these models in important areas like healthcare or finance. The researchers have created a new way of doing things that balances how well the model works with how easy it is to understand what’s going on inside the model. They’ve tested this approach and shown that it works better than other methods at making deep learning models more transparent. |
Keywords
* Artificial intelligence * Deep learning
