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I am applying the feature selection method, RFE (recursive feature elimination), from scikit-learn to a dataset. I do not have any pre-determined number of features for RFE and would rather get the number from data itself.

So far, I applied range of number of features, 1 to 10, for training data. For evaluation, I use the F1 from prediction outcome using the features from RFE. For serialization, I plan to use the number of features that provided the best F1.

What other methods may be used to determine the number of features for RFE? Thanks!

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I encourage you to look at this method https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV, it allows you to recursively test all of your features based on the scoring method of your choice including F1

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    $\begingroup$ You should add more info on the suggested method (links can be broken in the future) $\endgroup$ – Mark.F Dec 24 '18 at 21:22
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Generally, there are 3 Feature Selection methods:

  • Filter Methods
  • Wrapper Methods
  • Embedded Methods

I believe Feature Selection is totally overrated. But what do I know?

There's an amazing Feature Selection course on Udemy by Dr. Soledad Galli:

https://www.udemy.com/feature-selection-for-machine-learning/learn/v4/content

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  • $\begingroup$ Why do you feel feature selection is overrated? An explanation might help $\endgroup$ – HFulcher Feb 22 '19 at 20:29
  • $\begingroup$ (a) if you have 10 rows for each column, you should be fine (b) RFE sounds intellectually sound, but I've not seen a difference in accuracy of more than 0.10% $\endgroup$ – FrancoSwiss Feb 22 '19 at 20:32

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