Add "RepeatedStratifiedKFold" (from sklearn) on top of cross-validation in sequential feature selectors
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- Python
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Description
As reported by @nd26, adding "RepeatedStratifiedKFold" (i.e., repeating cv a user-specified number of times) may yield to better results on certain datasets. This could be added as an additional parameter to the SequentialFeatureSelector, for example, reapeat_cv, which should default to 0 and accept positive integers
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Research direction
Start at the SequentialFeatureSelector implementation and review how its cross-validation parameter is handled; compare the requested behavior with sklearn's RepeatedStratifiedKFold. Done means supporting a repeat-count parameter that defaults to 0 and accepts positive integers, with the repeated cross-validation behavior covered by tests.
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Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100