Support for more generic feature selectors
Open
enhancement
needs design
new component
- Dominant language
- Python
- Stars
- 850
- Forks
- 96
- PR merge metrics
- No merged PRs in 30d
Description
Currently, we only have SelectFromModel. It would be nice to support some feature selectors (ex: SelectKBest, SelectPercentile) that don't rely on an estimator and instead simply select features using statistical tests.
As I was working through the catboost PR (#247), I was not able to use a feature selector in the catboost pipeline because catboost boasts being able to handle categorical data, but the random forest classifier in our RFClassifierSelectFromModel feature selector could not, making it difficult to use in the catboost pipeline.
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