Machine Learning algorithm to predict an outcome where output is not known 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.
What I was looking for is called ordinal regression or ranking learning (https://en.wikipedia.org/wiki/Ordinal_regression)

Edit: I will elaborate more on why this solved my issue
What I was having a problem with was finding a statistical model to appropriately predict the ordinality of multiple independent options. What I cared about was the order of the options and less so about what the value of the options was.

Ordered Logit/Probit describe such models. The provided wikipedia link explains them in far more detail than I can.