Hello, I would like to use OneClassSVM for novelty detection. I have some 'normal' data for fitting the classifier. Then I have 'normal' and 'abnormal' data for testing the performance.
I would like to use the area under the ROC curve as the figure of merit of the detector. The function roc_curve needs the predicted probability. I have read that the probability can be obtained if the classifier is obtained with the parameter probability = True. However, I get an error when I try to pass this parameter. I am using version 0.10 of sklearn. For instance: import sklearn import sklearn.metrics import scipy import sklearn.svm X = scipy.random.randn(100, 2) X_train = scipy.r_[X + 2, X - 2] clf = sklearn.svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1, probability=True) Then I get an error. I have also tried clf = sklearn.svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1) clf.fit(X_train, probability=True) but it is again an error. Is that option available for OneClassSVM? If not, how could I draw the ROC? Could I sweep a threshold on the distance to the hyperplane given by clf.decision_function? Thank you Carlos ------------------------------------------------------------------------------ Introducing AppDynamics Lite, a free troubleshooting tool for Java/.NET Get 100% visibility into your production application - at no cost. Code-level diagnostics for performance bottlenecks with <2% overhead Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap1 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
