问题
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)?
回答1:
There would be no difference. For multi-label classification, sklearn one-versus-rest implements binary relevance which is what you have described.
来源：https://stackoverflow.com/questions/31456868/sklearn-difference-between-using-onevsrestclassifier-and-build-each-classifier