I have a fixed set of outcomes which should typically be determined by a fixed set of inputs (like a multi class problem).Here is a simple workflow:
initial inputs -> suggestion (prediction based on inputs) -> action (performed by humans) -> re-examine current inputs (inputs may change as a consequence of action) -> calculate rating (rating based on same inputs) -> decide if it was the right action or not (goal is to reach maximum rating of 10) -> send feedback to retrain
retraining data comes in once every few days. I'm not sure which kind of approach is best suited for such a problem. If I adopt a classification approach, I will have to create training data myself with sample scenarios ( since each of the independent variables having values between 0 to 10) and then come up with approximate suggestions and guess that if these suggestions are carried out, we get a higher rating.
But this also looks like a state problem to me, where we have the current state of inputs and our suggestions will move them to different value of inputs thereby increasing the overall rating.
I'm not sure of two things here a) Which kind of problem is this? Is it a classification or a MDP problem? b) With such little data coming in at such a large time gap, will either of them (classification / reinforcement) work well?