I was wondering whether I need different Hyper-parameters for my model depending whether I am random or scaffold splitting.
So lets say I want to compare the performance of my model with random and then scaffold split. Would I optimize the hyper-parameters for one of the two and then use the same ones for the other model. Or would I optimize them separately.
Is their a consensus or papers who have a similar issue?