Summary of Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models, by Donald Kridel et al.
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models
by Donald Kridel, Jacob Dineen, Daniel Dolk, David Castillo
First submitted to arxiv on: 31 May 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 Four prediction methods are compared and the best-known explainability techniques applied to identify feature importance (FI) in a static case. The FI set is then cross-checked under what-if scenarios for continuous and categorical variables in a dynamic case, revealing inconsistency between the two cases. The study summarizes the state of the art in model explainability and suggests further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers analyzed a loan dataset to understand how well prediction models can be explained. They tried four different methods and found that one worked best. Then, they used special techniques to see which features were most important for making predictions. But when they tested this idea in different situations, it didn’t work as expected. The study looks at what’s currently known about explaining model results and suggests more research is needed. |
