Thank you Peter and Andreas. That makes sense. Thanks for responding so
quickly.

Lukas


On Mon, Feb 4, 2013 at 10:40 AM, Ribonucleico <[email protected]>wrote:

>
>
> ---------- Forwarded message ----------
> From: Peter Prettenhofer <[email protected]>
> Date: Mon, Feb 4, 2013 at 10:08 AM
> Subject: Re: [Scikit-learn-general] Random forests: Measuring information
> gain in multi-output
> To: [email protected]
>
>
> Hi Lukas,
>
> the impurity (in your case entropy) is simply averaged over all
> outputs - see [1] - the code is written in cython (a python dialect
> that translates to C).
>
> best,
>  Peter
>
> [1]
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_tree.pyx#L1482
>
> 2013/2/4 Ribonous <[email protected]>:
> > I think I understand how a random forest classifier works in the
> univariate
> > case. Unfortunately I haven't found much information about how to
> implement
> > random forest classifier in the multi-output case.
> >
> > How does the random forest classifier in sklearn measure the information
> > gain for a given split in the multi-output case ? Can anyone point me to
> > references on this?
> >
> > Also, is the random forest implementation written in Python or another
> > language?
> >
> > Thanks,
> >
> > Lukas
> >
> >
> >
> >
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>
>
> --
> Peter Prettenhofer
>
>
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