Data Science Asked by Nouf on May 12, 2021
For classifying text into three classes question, complain and complements
where each sample can have multi-labels (question and complains, question and complements):
which approach is better when the data are labeled, unlabeled and unbalanced?
Generally there are two main options for multi-label classifications:
In theory there are $2^3$ possible classes for 3 labels, but in practice there might be less combinations in the data (as suggested in the question). If the labels depend on each other (for instance if it's unlikely to have a document which is both complain and complements), a joint model is more appropriate. However a joint model might need more instances to be trained properly, since it has more work to do than a binary model.
Answered by Erwan on May 12, 2021
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