I have a classification problem with both categorical and numerical data. The problem I'm facing is that my categorical data is not fixed, that means that the new candidate whose label I want to predict may have a new category which was not observed beforehand.
For example, if my categorical data was
sex, the only possible labels would be
other, no matter what. However, my categorical variable is
city so it could happen that the person I am trying to predict has a new city that my classifier has never seen.
I am wondering if there is a way to do the classification in these terms or if I should do the training again considering this new categorical data.