I have read this to learn about various method in multi-label classifiers. I learned that there are 3 techniques to do multi-label classifications:
1.Problem Transformation 2.Adapted Algorithm 3.Ensemble approaches
In the category of Problem transformations there are three more sub categories:
a.Binary Relevance b.Classifier Chains c.Label Powerset
I know that when we want better result we should apply the ensemble model. I would like to know in which situations the other different algorithm we should use?
I know how they differently work, but I do not know when I should use each of them maybe according to my use case or data!
Please let me know if my statements are not clear.