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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.

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I'm right alongside you in the learning process, but I'll give my two cents anyways.

I believe both methodogies would technically work, and you'd end up with either a score or a probability distribution for each label... but I believe it's a matter of interpretability and use case.

Intuitively, I'm partial to the latter suggestion of probabilities of each label.

I can imagine two use cases where you'd pick opposite approaches.

If you wanted to compare the predicted effectiveness of various potential new treatments, predicting a continuous "score" would allow you to more easily visualize treatments together in a scatterplot, for example.

However if you are building a model to interpret the individual effectiveness of new treatments, I think the class probability distribution would more clearer communicate the risk/benefit of a single treatment.

One downside to the continuous score metric is that a score of "4.0" might mistakenly imply that a treatment is "100% effective all the time", when in reality the definition of "definitely effective" might very greatly according to the expected patient outcome without treatment.

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