title: Sklearn: Difference between using OneVsRestClassifier and build each classifier individually
body: <p>As far as I know, multi-label problem can be solved with one-vs-all scheme, for which Scikit-learn implements <code>OneVsRestClassifier</code> as a wrapper on classifier such as <code>svm.SVC</code>. 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. </p>
  
  <p>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)? </p>