Yep, I know that. The PR looks promising, will look into it.
Just another question: If the OVR predicts multiple labels for a sample, are they somehow ranked? I know it is just the one vs rest approach, but maybe there is some kind of confidence involved. Because then the evaluation would be interesting, by looking at rankings. Regards, Philipp Am 24.01.2013 09:56, schrieb Joly Arnaud: > You should also be aware that the current metrics module doesn't handle > multilabels correctly. > > The following pr https://github.com/scikit-learn/scikit-learn/pull/1606 > might interest you. It had for multi-labels support for > some metrics. > > Best regards, > Arnaud Joly > > Le 23/01/2013 18:44, Andreas Mueller a écrit : >> Am 23.01.2013 18:39, schrieb Lars Buitinck: >>> if you want more predictions or something... >>> More in detail: OneVsRestClassifier exports an object called >>> label_binarizer_, which is used to transform decision function values >>> D back to class labels. By default, it picks all the classes for which >>> D > 0, but its threshold argument can be used to change that. >>> >>> So, if clf is an OvR classifier and >>> >>> D = clf.decision_function(x) >>> >>> for a *single sample* x contains no positive values, then >>> >>> # untested, may contain mistakes >>> clf.label_binarizer_.inverse_transform(D, threshold=(D.max() + >>> epsilon)) >>> >>> will predict at least one class label for x, namely the one with the >>> highest value according to the decision_function. The epsilon is >>> needed because inverse_transform compares values using >, not >=; set >>> it to a small value. Doing this for batches of samples is a bit more >>> involved. >>> >>> Of course, you can set the threshold to any value. Whether any of this >>> makes sense depends on your problem. >>> >>> [I used to be opposed to exporting the LabelBinarizer object on OvR >>> estimators, but I guess I should give up the struggle now -- this is >>> actually useful.] >>> >> I didn't even realize this possibility existed. I would have done it "by >> hand". >> Thanks for the instructions. >> >> ------------------------------------------------------------------------------ >> Master Visual Studio, SharePoint, SQL, ASP.NET, C# 2012, HTML5, CSS, >> MVC, Windows 8 Apps, JavaScript and much more. Keep your skills current >> with LearnDevNow - 3,200 step-by-step video tutorials by Microsoft >> MVPs and experts. ON SALE this month only -- learn more at: >> http://p.sf.net/sfu/learnnow-d2d >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > ------------------------------------------------------------------------------ > Master Visual Studio, SharePoint, SQL, ASP.NET, C# 2012, HTML5, CSS, > MVC, Windows 8 Apps, JavaScript and much more. Keep your skills current > with LearnDevNow - 3,200 step-by-step video tutorials by Microsoft > MVPs and experts. ON SALE this month only -- learn more at: > http://p.sf.net/sfu/learnnow-d2d > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Master Visual Studio, SharePoint, SQL, ASP.NET, C# 2012, HTML5, CSS, MVC, Windows 8 Apps, JavaScript and much more. Keep your skills current with LearnDevNow - 3,200 step-by-step video tutorials by Microsoft MVPs and experts. ON SALE this month only -- learn more at: http://p.sf.net/sfu/learnnow-d2d _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
