If a model is given a multilabel classification problem is it appropriate for it to then, as opposed to just predicting the given labels, use those labels to create a scoring scale of ordinal classification? For example, classifying how effective a drug is to treat a disease. I have training data with 4 labels (definitely effective, likely effective, possibly effective, and not effective). Can I encode these 4 labels to be numbers then get models to perform ordinal regression?
Or would it be more appropriate to have a model perform multilabel classification and take the probability calculated for each drug for the 'definitely effective' label and deem that as a score?
I am new to machine learning, so apologies if my question is rooted in error. For more information if useful, currently I am trying to compare logistic regression, random forest, gradient boosting and deep neural network.