Summary of Validity Of Feature Importance in Low-performing Machine Learning For Tabular Biomedical Data, by Youngro Lee et al.
Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data
by Youngro Lee, Giacomo Baruzzo, Jeonghwan Kim, Jongmo Seo, Barbara Di Camillo
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 work challenges the prevailing belief that tuning models to high accuracy is necessary for discussing feature importance in biomedical data analysis. The researchers show that low-performing models can still be used for feature importance analysis, provided the dataset size is adequate. They propose experiments to observe changes in feature rank as performance degrades sequentially and compare the results across synthetic and real biomedical datasets. The study reveals that feature cutting consistently shows better stability than data cutting when controlling for feature interactions. The findings suggest that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper challenges a common idea about biomedical data analysis. Researchers found that models don’t have to be super accurate to understand what features are important. They tested this idea using made-up and real datasets. The results show that even when the model isn’t very good, it can still figure out which features are most important if the dataset is big enough. This is an important discovery because it means that medical professionals might be able to get useful insights from their data even if their models aren’t perfect. |
