Thanks for the answer. I was expecting OCSVM to be not too much influenced by the increasing number of variables even if some of them are irrelevant. I am just wondering if the drop in performances is more likely to occur due to the overfitting of OCSVM or due to an unexpected behaviour of of Incremental PCA when the number of components is large.
For the moment the first one seems to be more likely. Please write me if you have other opinions. Thanks, Luca On Wed, Oct 14, 2015 at 1:02 PM olologin <ololo...@gmail.com> wrote: > On 10/14/2015 02:28 PM, Oliver Tomic wrote: > > I am not sure whether there is such a feature in scikit-learn, but the > cumulative (validated) explained variance after each component may also > give a good indication of when to stop including further components. that > is when it starts to drop. > > *explained_variance_ratio_ *attribute? > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > -- Sent by mobile phone
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