As a bit of a follow up to my previous question, I've seen disagreements online on whether I should center/scale my dummy variables prior to modeling. (look at the reprexes in the above link for an example). Andrew Gelman seems to say that I shouldn't, but Rob Tibshirani seems to say that I should.
Does anyone have any experience in this? Would it differ on whether I was using glmnet/LASSO vs keras/neural network?
(One of my favorite things about tree-based models like xgboost is that I don't have to think about these issues as much )