I have 13 small datasets from 12 different countries. All datasets have the same outcome and features, though have a different number of observations (ranging from ~50 to ~800). I would like to combine these datasets in a ML model.
Based on the answer to this question (Is it advisable to combine two datasets?), I can simply include a feature identifying the source of the data to control for potential bias.
Assuming this is true and extending on that question, what is an appropriate way to split the data into training/testing sets (ie, should I sample such that each dataset has the same representative proportion)? With 13 datasets, would it be advisable to leave out 2-3 datasets entirely from model development for external validation, and if so what would the decision-making process be for that?