I have some data set and need to use a few classification methods to make prediction. I first need to pre-process the data set.
France is administratively divided in regions (13), and regions are divided in departments (96). Then at a smaller level you have towns.
My data set contains quite a few predictors, including "regions", "departments", "towns".
It seems obvious that those 3 predictors will be correlated as for instance: if department = "Finistère" then regions = "Bretagne" ; if department = "Morbihan" then regions is also equal to "Bretagne".
So what should I do with those kind of variables, that are "included" into other variables ? Should I take the one with more factor levels (here towns) ? Or maybe town is too specific and I should keep departments ?
In my case, the outpout is either 1, 0 or -1 (3 categories), so SVM would not work.
The classifiers I'm gonna use are sensitive to varible correlation I think.
What would be the best this to do in this case ?