# Feature Selection with BorutaShap

**URL:** <https://forum.numer.ai/t/feature-selection-with-borutashap/4145>\
**Category:** Data Science\
**Created:** [September 17, 2021, 11:04pm UTC](https://forum.numer.ai/t/feature-selection-with-borutashap/4145 "2021-09-17T23:04:24Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![objectscience](https://yyz1.discourse-cdn.com/flex009/user_avatar/forum.numer.ai/objectscience/32/2913_2.png) [@objectscience](https://forum.numer.ai/u/objectscience)\
**Post date:** [December 6, 2021, 8:20pm UTC](https://forum.numer.ai/t/feature-selection-with-borutashap/4145/11 "2021-12-06T20:20:57Z")

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Might be worth noting that it’s possible to get:  
Min era for train = 1  
Min era for test = 2

But the eras will still be shuffled to something like this:  
train = [1, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 17, 19, 20, 23, …, 573, 574]  
test = [2, 3, 12, 13, 16, 18, 21, 22, 25, …, 564, 568]

So we’re effectively training on data that occurs after eras in the test set. I don’t know how big of an issue it is for feature selection, but forcing a proper time series split doesn’t seem like a bad idea here.

I know @mdo created a custom splitter [here](http://forum.numer.ai/t/era-wise-time-series-cross-validation/791), maybe we can use that again with a little tuning.

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