2011/12/29 Gael Varoquaux <[email protected]>: > On Thu, Dec 29, 2011 at 12:46:36PM -0800, adnan rajper wrote: >> I use LinearSVC for text classification. My problem is that I want to >> generate ROC curve for LinearSVC. Since LinearSVC does not output >> probabilties. Is there any other way to generate ROC curve for LinearSVC? >> I have tried svm.SVC(kernel='linear', probabilities=True) but it gets too >> slow. > > If you really want to use LinearSVC (you could use a LogisticRegression > instead), I see a couple of ways: > > * You could bootstrap the prediction, and thus get some decision metric. > This is quickly going to get costly. > > * You could use the decision function, (decision_function method of the > LinearSVC) although this is clearly a hack.
Why is this a hack? ROC is only concerned with the relative positions of the decision threshold, not the probability normalization AFAIK, am I wrong? -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Ridiculously easy VDI. With Citrix VDI-in-a-Box, you don't need a complex infrastructure or vast IT resources to deliver seamless, secure access to virtual desktops. With this all-in-one solution, easily deploy virtual desktops for less than the cost of PCs and save 60% on VDI infrastructure costs. Try it free! http://p.sf.net/sfu/Citrix-VDIinabox _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
