hi guys, the scale_C is not released yet and not setting it in the current release raises a warning. But maybe we could be even more explicit to warn users.
right now C is None by default and defaults to n_samples which amounts to the C=1 with scale_C=False which is the default behavior of libsvm. without the scale_C the libsvm/liblinear bindings are the only models whose hyperparameters depend on the training set size. @james : what do you think could make things better? Alex On Sat, Mar 17, 2012 at 2:04 AM, Andreas Mueller <[email protected]> wrote: > On 03/17/2012 01:55 AM, Lars Buitinck wrote: >> Op 17 maart 2012 01:30 heeft Andreas<[email protected]> het >> volgende geschreven: >>> If we change the API, I would go for alpha as the current >>> "scale_C=True" but optionally provide the "C", which behaves >>> like the LibSVM parameter. >> You mean we'd have two regularization parameters? I'd find that confusing. >> > What would you prefer? > > Not having a "C" parameter is also somewhat confusing. And having a > "C" parameter that does something different than the "C" parameter > of the software we are wrapping is also pretty confusing. > > ------------------------------------------------------------------------------ > 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 ------------------------------------------------------------------------------ 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
