Sklearn: Difference between using OneVsRestClassifier and build each classifier individually

Question

As far as I know, multi-label problem can be solved with one-vs-all scheme, for which Scikit-learn implements OneVsRestClassifier as a wrapper on classifier such as svm.SVC. I am wondering how would it be different if I literally train, say we have a multi-label problem with n classes, n individual binary classifiers for each label and thereby evaluate them separately.

I know it is like a "manual" way of implementing one-vs-all rather than using the wrapper, but are two ways actually different? If so, how are they different, like in execution time or performance of classifier(s)?

Answer 1

There would be no difference. For multi-label classification, sklearn one-versus-rest implements binary relevance which is what you have described.