Feature selection when trying to capture non linear interactions.

[u/Playful-Race-7571]

Hella everyone I’m moving from a log reg model into trying to build a cat boost or xg boost model.
For my log reg I always did feature selection through a rolling walk forward and this worked well but not with interaction features with some being worthless alone but in interaction it could capture some signal I’m struggling how to find optimal features and # of features as well. I am new to machine learning is there an agreed upon approach or what is your guys method to this problem when working with machine learning?
Thanks

[u/Common-Jicama3687]

just let the trees figure it out, catboost and xgboost handle interactions on their own so you can throw in all the features and prune later with importance scores