Am 15.01.2012 19:45, schrieb Gael Varoquaux: > On Sun, Jan 15, 2012 at 07:39:00PM +0100, Philipp Singer wrote: >> The problem is that my representation is very sparse so I have a huge >> amount of zeros. > That's actually good: some of our estimators are able to use a sparse > representation to speed up computation. > >> Furthermore the dataset is skewed so one class takes a huge amount of >> labels and another one is also pretty high. >> I have successfully used logistic regression and I could achieve a >> recall of about (in the best case dataset) 65%. I am pretty happy with >> that result. But when looking at the confusion matrix the problem is >> that many examples get mapped to the large class. > Use "class_weight='auto'" in the logistic regression to counter the > effect of un-balanced classes. > > For SVMs, the following example shows the trick: > http://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html > > HTH, > > Gael > > ------------------------------------------------------------------------------ > RSA(R) Conference 2012 > Mar 27 - Feb 2 > Save $400 by Jan. 27 > Register now! > http://p.sf.net/sfu/rsa-sfdev2dev2 > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general Thanks a lot for the help! This helped out quite a bit. But I am still not entirely happy with the results. Maybe some further ideas?
Thanks a lot Philipp ------------------------------------------------------------------------------ Keep Your Developer Skills Current with LearnDevNow! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
