Summary of Statistical Test For Feature Selection Pipelines by Selective Inference, By Tomohiro Shiraishi et al.
Statistical Test for Feature Selection Pipelines by Selective Inference
by Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino, Ichiro Takeuchi
First submitted to arxiv on: 27 Jun 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 This paper proposes a novel statistical test to assess the significance of data analysis pipelines in feature selection problems. The approach enables the development of valid statistical tests applicable to any feature selection pipeline composed of predefined components, using selective inference techniques. As a proof of concept, the authors consider feature selection pipelines for linear models and demonstrate the effectiveness of their method through experiments on synthetic and real data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out if our way of selecting features in data is meaningful or just by chance. They created a new test to make sure we’re not picking random features that look important but aren’t. This test works for any combination of algorithms used to select features, like imputing missing values or detecting outliers. The authors tested their method on fake and real data and showed it works well. |
Keywords
* Artificial intelligence * Feature selection * Inference
