I have a multi-label classification problem-- millions of records that potentially could hold more than one label. I'm running into issues related to lack of research/examples online, and am unable to create a strong model. Would it be advised against to build a multi-class classification model, and take any label with a predicted probability over 0 to be considered a potential label? Even with problem transformations, like binary relevance and classifier chains, the accuracy is terribly low (~12%) as opposed to using Naive Bayes and getting ~57% accuracy.


Perhaps I understood the question wrong, but multi-label and multi-class problems are fundamentally different, as the multi-class is exclusive (no instance can be classified into two classes a the same time), while the multi-label relaxes this condition. As Wikipedia on multi-label states:

in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to.

So, using multi-class or multi-label depends on the problem itself and not on how you solve it.

|improve this answer|||||

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.