machine learning - How to use the OneVsRestClassifier in Scikit-learn to analyse the performance of predicting each individual class with multi-class classification? -

in onevsrestclassifier documentation on scikit-learn website states following:

"since each class represented 1 , 1 classifier only, possible gain knowledge class inspecting corresponding classifier."

but gives no explanation of how , can't see how of methods in documentation on page achieve that. want able print out accuracy of model each individual class, can see performance has @ predicting each class.

the code have far below, don't know go here, seems there nothing in documentation, explains how this. appreciated.

def predict_one_vs_rest(self):     clf = onevsrestclassifier(linearsvc(random_state=0))     clf.fit(self.x, self.y)     result = clf.classes_     estimators = clf.estimators_     print(result)     print("")     print(estimators) 

you don't need wrap linearsvc in onevsrestclassifier. documentation says, linearsvc supports multi-class classification. inspecting accuracy of classes, use confusion matrix or classification report, example.