Hi, You can do: ``` from sklearn.metrics import roc_curve, auc
X_test = np.r_[ND, AD] pred = clf.predict(X_test) y_true = np.array([0] * 500 + [1] * 500) fpr, tpr, thresholds = roc_curve(y_true, scoring) # then you can plot(fpr, tpr) to get the roc curve and compute the AUC with: AUC = auc(fpr, tpr) ``` Best, Nicolas 2015-10-20 3:41 GMT+02:00 Ady Wahyudi Paundu <awpau...@gmail.com>: > Hi all, > > Can I create ROC curve for one_class_SVM classifier? > If I can, can you give pointer on how to do this? (or a link?) > > for example now i have: > LD: normal data for learning (100 item) > ND: normal data for evaluation (500 item) > AD: abnormal data for evaluation (500 item) > > one_class_SVM code for evaluation would be something like this (NU and > GA is user input): > > clf = svm.OneClassSVM(nu=float(NU), kernel="rbf", gamma=float(GA)) > clf.fit(LD) > y_pred_train = clf.predict(LD) > y_pred_test = clf.predict(ND) > y_pred_outliers = clf.predict(AD) > n_error_train = y_pred_train[y_pred_train == -1].size > n_error_test = y_pred_test[y_pred_test == -1].size > n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size > > how to produce ROC curve from there? > > Thanks in advance > ~Ady > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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