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Allow **fit_params on RFECV #17954

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ciberger opened this issue Jul 20, 2020 · 5 comments
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Allow **fit_params on RFECV #17954

ciberger opened this issue Jul 20, 2020 · 5 comments
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Hard Hard level of difficulty module:feature_selection New Feature

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@ciberger
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Hi,

minor change in fit method enabling extra fit parameters (i.e. **fit_params).

Link to source code:
https://github.com/scikit-learn/scikit-learn/blob/fd237278e/sklearn/feature_selection/_rfe.py#L486

Thanks!
@thomasjpfan
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This would first require RFE to support **fit_params.

There is related work for getting RFE to support sample weights: #7333

@cerlymarco
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Everyone looking for RFE with Gradient Boosting, like LGBM or XGB, I suggest shap-hypetune... A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

It supports RFE (also with shap feature ranking) with every fitting parameters like in the standard algorithm API

@fingoldo
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For RFE, **fit_params are implemented & merged with #20380. Can someone enable them in RFECV as well, please?

@cmarmo cmarmo mentioned this issue Dec 16, 2021
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@ScottMGustafson ScottMGustafson mentioned this issue Jul 26, 2022
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@thomasjpfan thomasjpfan added the Hard Hard level of difficulty label Jul 27, 2022
@thomasjpfan
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I'm marking this has Hard based on #24004 (comment) where we want to use the metadata routing API for routing the fit_params.

@adrinjalali
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solved now with metadata routing.

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[MRG] Add fit_params to RFECV.fit ScottMGustafson/scikit-learn
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@cmarmo @adrinjalali @thomasjpfan @fingoldo @ciberger @cerlymarco