# In which situation different methods in multi-label classifications in scikit learn should be applied?

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
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.

Thanks,