I have a very unique problem that I would like to solve using machine learning. I have a set of 90 or so unique options. Each of these options has a unique set of features (5 to be specific) that vary over time and that can be represented mathematically as either scalars or booleans. I want to train a model to tell me what the best option at that moment is given the complete set of inputs.
At first, I figured a simple feedforward neural network would do the trick but I don't have a ton of experience with ML models so I ran into the following issue. I know my problem isn't really a classification problem since I am trying to pick the best option. I figured it could be easily modelled as a regression problem (as some mathematical combination of the inputs) which is fine but then I don't know what the expected outcome would be. For example, if the expected output is the weighted average of the inputs, I don't know what the final value should be. All I know is that option B should be suggested over option A.
I started reading about random forests and they seem like they might help my situation but I wanted to get other opinions first.