For the task of churn modelling I was considering:
- Compute k clusters for the data
- Build k models for each cluster individually.
The rationale for that is,that there is nothing to prove, that the population of subsribers is homogenous, so its reasonable to assume that data-generating process may be diffrent for diffrent "groups"
My question is, is it an appropriate method? Does it violate anything, or is it considered bad for some reason? If so, why?
If not, would you share some best practices on that issue? And 2nd thing - is it generally better or worse to do preclustering than model tree (As defined in Witten,Frank - classification/regression tree with models at the leafs. Intuitively it seems that decision-tree stage is just another form of clustering, but idk if it has any advantages over "normal" clustering.).