I'm researching a regression model to predict a target value that has four features, all of which are categorical.
The categories are not fixed, e.g. one is a customer identifier. How could my model handle making a prediction for a customer identifer it hasn't seen before, based on the remaining features it has been trained with?
I have considered having a model for each of the features that could predict which category label is most similar based on the other three remaining features (or multiple similar category labels could be used and take an average of the target values for those).
My only concern with with this method is that it's not that scalable, I'm going to want to extend the model with more and more categorical features.
Is there some technique that could create an 'unknown' label for each of the features so the model can handle this case or would the prediction likely be completely inaccurate?