OK, thank you. I will do it in that way Carlos
Quoting [email protected]: > Today's Topics: > > 1. Re: ROC for OneClassSVM (Andreas Mueller) > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 06 May 2013 12:33:03 +0200 > From: Andreas Mueller <[email protected]> > Subject: Re: [Scikit-learn-general] ROC for OneClassSVM > To: [email protected] > Message-ID: <[email protected]> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > On 05/06/2013 12:27 PM, [email protected] wrote: >> 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? >> > Yes, I think this is what you should do. > Hth, > Andy > > ------------------------------------------------------------------------------ Learn Graph Databases - Download FREE O'Reilly Book "Graph Databases" is the definitive new guide to graph databases and their applications. This 200-page book is written by three acclaimed leaders in the field. The early access version is available now. Download your free book today! http://p.sf.net/sfu/neotech_d2d_may _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
