Comments (2)
exactly the information I was looking for! Thank you @aerdem4 for the details, this quite helps.
Generalization is the key here then (hence custom validation/evaluation). And the grouping idea.. I haven't seen it supported before --very neat!
Please feel free to mark this issue close/resolve and thanks again for the prompt reply!
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Hi @skadio, thanks for the question. Since there may be different implementations of RFE, I will compare with sklearn implementation:
1- LOFO allows you to pick your own validation scheme. You can have time split, groupkfold or even a custom split. RFE ignores the validation set performance, hence the generalization. LOFO can tell you not only important features but also harmful features which cause overfitting.
2- Similarly, your model doesn't have to have feature importance attribute like sklearn RFE wants. Also you can pick any evaluation metric you want.
3- LOFO allows you to group features. Let's say you have 900 features representing cell responses to a drug. You can group them into one.
So this package doesn't invent anything new but try to encapsulate the best practice to get realistic feature importance. Please let me know if you have further questions.
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Related Issues (20)
- How to use GroupKFold? HOT 10
- Add logging or restart mechanism HOT 2
- Sample_weight? HOT 2
- Add the choice between Mean/Std and Median/IQR HOT 5
- Having a lot of features + Using LOFO? HOT 3
- Multiclass models HOT 4
- Groupkfold or Groupshufflesplit Cross Validation HOT 1
- Support multiclass classification ? HOT 2
- TimeSeriesSplit with Lofo HOT 1
- Feature selection using statistical significance
- How to perform feature selection with hyperparameter tuning?
- Returns NaNs all the time HOT 1
- Any tutorial for dealing with genetic data? HOT 2
- Could you add a reference? HOT 1
- Running the example in the readme throws errors
- Compatibility with neural network: replacing with constant value instead of dropping the feature HOT 2
- requirements.txt not packaged in source distribution
- Pandas 2.0.x compatibility HOT 5
- Variable Grouping Only Works When Model Parameter is Kept To Default HOT 5
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