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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)
The text was updated successfully, but these errors were encountered:
This is related to discussions regarding back-transforming an outcome column.
Recipe steps have the
skip
option, which, whenTRUE
, 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 likepredict()
oraugment()
when used outside of the aforementioned functions.(I'm not dead set about the proposed argument name)
The text was updated successfully, but these errors were encountered: