Is it advisable to use RFE for linear or logistic regression when we have some dummy features. Reason I am asking this is: in RFE we will eliminate some features which will also include dummy features and as per this answer for a categorical variable which is dummy encoded we should not keep some features & drop others, we should keep all dummy features of a categorical feature.
Reasoning given is : "You should leave all five indicator variables in. Dropping predictors because they are non-significant leads to biased estimates for regression coefficients and inflated p-values."
"The problem with dropping the indicator is that you'll change the p-values of the remaining levels as well, as you're shifting the intercept (aka the reference group.)"
If ideal thing to do is keeping all levels/dummy features of a categorical variable then how do you use RFE for dummy features or how do you eliminate unimportant dummy or categorical features?