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Ekeany / Boruta-Shap

A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.
MIT License
559 stars 86 forks source link

Shadow features in the final features #72

Closed ivanpanshin closed 2 years ago

ivanpanshin commented 2 years ago

In the end we get a DataFrame with features and their importance + number of selected features. Why do we still have shadow features at this point? Shouldn't we eliminate them?

Also, suppose Boruta says that we should select top-10 features. If some shadow_features are in top-10, they are also counted, right?

Ekeany commented 2 years ago

The shadow features should not be included in the final dataset as they are just random features used for selecting the which features are important.

ivanpanshin commented 2 years ago

Then why don't you filter them out by default by the end of the training?

Ekeany commented 2 years ago

That is what should have done, but I forgot to implement it. Thanks, for spotting this bug.

On Mon 18 Oct 2021, 07:48 Ivan Panshin, @.***> wrote:

Then why don't you filter them out by default by the end of the training?

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ivanpanshin commented 2 years ago

Okay, great :) Then I guess you should also fix a number of selected/accepted features that BORUTA returns since sometimes they include shadow features

Ekeany commented 2 years ago

Thanks!

On Mon 18 Oct 2021, 22:06 Ivan Panshin, @.***> wrote:

Okay, great :) Then I guess you should also fix a number of selected/accepted features that BORUTA returns since sometimes they include shadow features

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