I think it will depend on the multiclass LR objective used. Depending on
the objective, we need to learn n_classes vectors or n_classes - 1 vectors.
In the former case, a multiclass LR will do twice more work as a binary LR.
One advantage of OvA is that it is embarrassingly parallel w.r.t. classes.
Requiring users to use OneVsResClassifier kind of goes against the
principles we have followed so far IMO.
M.
On Fri, Mar 7, 2014 at 7:05 PM, Vlad Niculae <zephy...@gmail.com> wrote:
> In some cases it might be preferable to fit an OvA model. In those
> cases, I think the
> user code would look nicer and more explicit if it'd use the
> sklearn.multiclass.OneVsRest encoder.
>
> The downside is that we'll need to go through an ugly deprecation cycle
> for a major class in the library.
> With the long term in mind I agree with Lars.
>
> My 2c,
> Vlad
>
> On Fri Mar 7 10:54:19 2014, Lars Buitinck wrote:
> > 2014-03-06 18:41 GMT+01:00 Manoj Kumar <manojkumarsivaraj...@gmail.com>:
> >> I have prepared a wiki page for the first draft of my GSoC proposal
> after
> >> several discussions. Please do have a look and provide me feedback.
> >>
> https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014-Application:-Improved-Linear-Models
> >
> > Technical question: do we need separate LR and multinomial LR? If CD
> > can fit multinomial LR instead of OvA LR, then I'm +1 on merging
> > MultinomialRegressionCV and LogisticRegressionCV. That may leave more
> > time for niceties like documentation :)
> >
> >
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