Objects which support multiple-output regression (aka multi-task
regression):
- SGDRegressor
- LinearRegression
- Ridge
- OrthogonalMachingPursuit
- PLSRegression (the only one which actually makes use of the label
correlation?)
...
Not that bad!
I've started a PR to summarize the estimator capabilities:
https://github.com/scikit-learn/scikit-learn/pull/804
This is an ongoing effort and I need your help!
Thanks,
Mathieu
On Mon, Apr 30, 2012 at 9:11 PM, Vlad Niculae <[email protected]> wrote:
> For now, lasso (and some others) can be invoked through the sparse_encode
> function and it does the multitarget wrapping automatically over multiple
> cores. Just pay attention to the shapes of the inputs since they need to be
> transposed (the function makes sense in a dictionary learning context).
>
> On Apr 30, 2012, at 14:48 , Alexandre Gramfort wrote:
>
> > for linear models it's in the todo list from Vlad's GSOC proposal.
> >
> > it's already possible for Ridge and OMP
> >
> > Alex
> >
> > On Mon, Apr 30, 2012 at 12:28 PM, Andreas Mueller
> > <[email protected]> wrote:
> >> Am 30.04.2012 12:26, schrieb Justin Bayer:
> >>> I'd be interested in the linear models, Gaussian processes, trees, KNN
> and SVMs.
> >>>
> >>> To be honest, I did not try them all out recently (last time november
> >>> or so), I just skimmed the docs and checked whether anything changed.
> >>>
> >>>
> >> For KNN, it should be very easy to add. With linear models, I guess
> >> it depends. For SVMs, we don't have a chance as we wrap libsvm.
> >> For trees, it is possible in principal but not with our implementation
> >> afaik,
> >> so a wrapper would be good.
> >>
> >>
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> ------------------
> Vlad N.
> http://vene.ro
>
>
>
>
>
>
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