I have 10 categorical features and a multi-class target.
Training data contains rows where the same 10 categorical features may map to a different target class.
What classification algorithm should I choose that fulfils the following criteria:
- prediction output should only show results if the prediction input matches a row example that existed in the training data. (I don't want to make assumptions on inputs it has never been trained on, as this model will deal with people's lives in the real world)
- prediction output should be targets that match exactly the prediction input, ordered by their frequency in the training data, with the highest as 'the best match' at the top
I realize this looks like it could be 'easily' solved with databases but I would like to use Classification and train a model instead of having a cumbersome db infrastructure with columns, keys, indexes and other boring things.