+1 for seeing this implemented. I feel it would be a useful addition for
work we do here that involves use of random forests.
On Mon, Sep 8, 2014 at 3:14 PM, Gilles Louppe <g.lou...@gmail.com> wrote:
> Hi Luca,
>
> This may not be the fastest implementation, but random forest
> proximities can be computed quite straightforwardly in Python given
> our 'apply' function.
> See for instance
>
> https://github.com/glouppe/phd-thesis/blob/master/scripts/ch4_proximity.py#L12
>
> >From a personal point of view, I never use them but since this is
> quite standard in other random forest implementations, this may be a
> nice little contribution. I dont know where it should be put though in
> scikit-learn, since it very much looks like a pairwise metric.
>
> What do other tree growers think?
>
> Cheers,
> Gilles
>
> On 8 September 2014 11:05, Luca Puggini <lucapug...@gmail.com> wrote:
> > Hi,
> > for personal reason I am writing a function to compute the outlier
> measure
> > from random forest
> > http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#outliers
> >
> > with a little more work I can include the function in the sklearn random
> > forest class.
> >
> > Is the community interested? Should I do it?
> > I think that this would be useful. This function is already available in
> > matlab
> http://www.mathworks.co.uk/help/stats/compacttreebagger-class.html
> >
> > Let me know.
> >
> > Best,
> > Luca
> >
> >
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