2011/12/5 Ian Goodfellow <[email protected]>: > > ok, I was using LinearSVC, so I guess I am still not using the dense > implementation. > > Is there a way to use one-against-rest rather than one-against-many > classification with the SVC class?
What is one-against-many? SVC mutliclass support comes directly from the underlying C++ implementation from libsvm which is one-against-one. > This page makes it sound like the > primary difference between SVC and LinearSVC is that SVC uses > one-against-one while LinearSVC uses one-against-rest: > http://scikit-learn.sourceforge.net/dev/modules/svm.html The main difference is the implementation: SVC is based on libsvm and LinearSVC is based on liblinear. - libsvm uses SMO (a dual solver) and supports non-linear kernels and has complexity ~ n_samples^3 hence cannot scale to large n_samples (e.g. more than 50k). - liblinear uses some kind of fancy coordinate descent (primal or dual solvers) optimized for regularized linear models, provides more regularization / loss function options such as l1 penalty and can scale to large n_samples (as long as the sparse internal representation of the data fits in memory). > By the way, I suggest someone update the documentation to specify what > the consequences of using the different SVM classes are. Currently > LinearSVC is recommend "for huge datasets", not "for huge sparse > datasets." That is on > this page: > http://scikit-learn.sourceforge.net/dev/modules/generated/sklearn.svm.LinearSVC.html For huge dense data, the only viable option is SGDClassifier on memory mapped arrays (double precision). -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
