How to treat data transformation choices as hyperparemeters?
So, the question talks about how to treat transformation choices as hyper parameters.
How I would go about it is the following:
Use one baseline model architecture for the data and then repeat the following:

Instantiate the baseline model (effectively make sure all of the weights are initialised)
Create the transformed dataset
Train the model
Compute generalisation performance measures (AUC, precision, recall, whatever).

Then compare the generalisation performance across all of the data transforms to find the &quot;best&quot; transformation which improves a generalisation metric which is appropriate for your task.