I am asking if there already exist approaches and researches on the following topic.
Imagine there are 10 stores and in 3 stores labeled training data was available, so I built 3 classification models based on those datasets (e.g. to classify my customers into A, B, C customers).
All stores basically have similar characteristics, but Model A will work best with Store A, Model B best with Store B, etc. because of geographical or cultural differences.
If training data is not available for my other 7 stores, is there any approach to find out if I should use Model A, B or C for my Store D data?
I assume the data distribution of Store D must be compared with those of A, B and C to estimate which model will work best. Any ideas or recommendations of existing papers about that topic?