I was reading this post about a reproducibility tool when I realized that the author decided to split the train/test datasets before featurization.
It didn't make sense to me, because why would you not be able to commute those operations? But just a few days later I found this comment in a deepchem repo issue:
In cases of data augmentation, there are use-cases when it might make sense to featurize data after it has been split into train/test/validation.
And I have no idea how that would be a thing, but apparently it is. What kind of data augmentation - or any other data transformation for that matter - could justify postponing the dataset splitting?