I am intending to use penalized ordinal regression (the shrinkage method used is elastic net) as I have ~ 50 variables (using the
ordinalNetpackage in R). My past experience with transforming IVs have been rather manual, inspecting residuals, fitting 'binned' effects and seeing if the relationship is linear etc. This obviously doesn't scale to situations with a large number of variables!
If I wanted to explore IV transformations in an automated way, does it make sense to generate all sensible transformations for IV's and supply this saturated model (incl. all transformations) to elastic net? I am having trouble finding examples of people using regularization to select specific transformations of an IV -- maybe this is because it is not recommended.
[EDIT]: Or maybe this is because regularization is moving more into the 'machine-learning' space, where transformations of the IVs are uncommon? It's been mentioned before here: why-arent-power-or-log-transformations-taught-much-in-machine-learning
I appreciate any advice. Thanks!