If your data is really dense, then you should try to use the SGDClassifier model instead of LinearSVC. It has an implementation for dense numpy arrays hence will use twice as less memory as a sparse representation.
However I am pretty sure that it will force a copy of your data to be double precision (64bit). If you install cython you can patch the source code to force single precision instead. We might want to add support for single precision for SGDClassifier and other models in the future although this is not planned yet. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ All of the data generated in your IT infrastructure is seriously valuable. Why? It contains a definitive record of 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-d2dcopy2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
