2014-09-27 4:51 GMT+02:00 Mathieu Blondel <math...@mblondel.org>: > This is because LinearSVC doesn't support sample_weight. > > I added a new issue for raising a more explicit error message: > https://github.com/scikit-learn/scikit-learn/issues/3711 > > BTW, a linear combination of linear models is a linear model itself. So you > can't learn a better model than a LinearSVC() with > AdaBoostClassifier(svm.LinearSVC())
While adaboosted linear SVM and vanilla linear SVM are both linear models, they don't optimize the same loss: the loss of the boosted model automatically puts more weights on samples that are harder to classify (closer to the decision hyperplane, or on the wrong side of the optimal hyperplane). Therefore, adaboosted linear models might or might not be better than non-boosted linear models. I think it depends on the amount of label noise that might cause the boosted models to overfit some noisy samples outliers. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer http://pubads.g.doubleclick.net/gampad/clk?id=154622311&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
