CODEWITHSUNDEEP
machine-learningscikit-learn
Rich Gray
Rich Gray

Reputation: 201

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

In the OneVsRestClassifier documentation on the scikit-learn website it states the following:

"Since each class is represented by one and one classifier only, it is possible to gain knowledge about the class by inspecting its corresponding classifier."

But it gives no explanation of how to do this and I can't see how any of the methods in the documentation on this page could achieve that. I want to be able to print out the accuracy of the model for each individual class, so that I can see the performance it has at predicting each class.

The code I have so far is below, but I really don't know where to go from here, as it seems that there is nothing in the documentation, which explains how to do this. Any help is much 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)

Upvotes: 5

Views: 5034

Answers (1)

Andreas Mueller
Andreas Mueller

Reputation: 28748

You don't need to wrap LinearSVC in OneVsRestClassifier. As the documentation clearly says, LinearSVC already supports multi-class classification. For inspecting accuracy of the classes, you could use the confusion matrix or the classification report, for example.

Upvotes: 4

Related Questions