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Repeatedly withholding subsets of the data during model fitting in order to quantify the model performance on the withheld data subsets.

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Is it ok to keep/discard rules based on the holdout set?

We have a POC project that is looking for rules that fit our data (eg "when a=1 and b=2 and c=3 then X=6" sort of thing). We split our data into 6 sets, and we use the first 5 sets as K-fold training …
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