Le 21 mars 2012 11:14, Mathieu Blondel <[email protected]> a écrit : > On Mon, Mar 19, 2012 at 1:22 AM, Andreas <[email protected]> wrote: > >> Are there any other options? > > Another solution is to perform cross-validation using non-scaled C > values, select the best one and scale it before refitting with the > entire dataset (to take into account that the entire dataset is bigger > than a train split). > Injecting estimator-specific code in GridSearchCV would be dirty so a > SVCCV class could be added. Note that, in my opinion, such a class > should be added anyway: currently the grid search throws away the > kernel cache even though it could be reused across folds (unless the > parameter is a kernel one). Reusing kernel cache makes it hard to > parallelize the grid search but I wouldn't be surprised if a > sequential approach with shared kernel cache is faster than a parallel > approach with separate kernel cache.
I am pretty sure that warm restarting the support vectors active set would help too if we are to compute a regularization path. Unfortunately I don't think the public C++ API of libsvm makes that easy / possible... -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
