Hi Mamun, from sklearn.metrics import roc_curve, auc from sklearn.svm import OneClassSVM
ocsvm = OneClassSVM().fit(X_train) scoring = - ocsvm.decision_function(X_test) # the lower, the more normal fpr, tpr, thresholds = roc_curve(y_test, scoring) AUC = auc(fpr, tpr) HTH Nicolas 2016-06-06 19:21 GMT-04:00 Mamun Rashid <[email protected]>: > Hi Nicolas, > Thanks for your reply. Apology for the naive question. > I can see from the example that we can plot the decision boundary using > the decision function. > Not sure how can I extract the ROC and PRC metric from there. A small > example would greatly help. > > Thanks, > Mamun > > On 3 Jun 2016, at 17:16, Nicolas Goix <[email protected]> wrote: > > Hi Mamun, > You can draw ROC and PR curves using the OCSVM decision_function > Nicolas > > 2016-06-03 11:54 GMT-04:00 Mamun Rashid <[email protected]>: > >> Hi everyone, >> I am running OneClassSVM method. It seems unlike the normal SVC, which >> has an option to return probability, this method does not have any option >> to retrieve probability values. >> I would like to draw some performance metric such as the ROC and >> Precision Recall about the performance of the classifier. >> >> Thanks, >> Mamun >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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