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This is related to discussions regarding back-transforming an outcome column.
Recipe steps have the skip option, which, when TRUE, does not apply the step to new data (i.e., only to the training set).
While not the same thing, we might think about having a mandatory flag called something like devel_only that specifies that we only apply it during the model development cycle (i.e., in the tuning/resampling/last_fit/best_fit cycles).
We could default to devel_only = FALSE for almost everything, but it would allow users to only execute it during functions like predict() or augment() when used outside of the aforementioned functions.
(I'm not dead set about the proposed argument name)
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topepo commentedon Feb 20, 2025
I like
new_data_onlymore thandevel_only.I think that the steps would be
predict()to conditionally apply the fitted adjustments.Right now I think that we would only have this affect
predict()(and notfit()).