Here: https://github.com/scikit-learn/scikit-learn/pull/1176
On 29 July 2014 21:59, Lars Buitinck <[email protected]> wrote:
> 2014-07-28 23:46 GMT+02:00 Mario Michael Krell <[email protected]>:
> > I have to somehow contradict. In fact it would be possible to get a
> > probability but it requires some "work". So it is not easy.
> >
> > I my group, we are using a sigmoid fit introduced by Platt to map SVM
> scores
> > to probability values. We integrated it in our pySPACE framework, which
> also
> > interfaces scikit-learn algorithms. Unfortunately for using the fit
> together
> > with kernels additional/separate data is required for training to avoid
> > over-fitting.
>
> "Easy" was indeed the keyword here. Isotonic calibration is even
> better than Platt scaling, but hasn't been implemented in scikit-learn
> master either (there's a PR for it somewhere).
>
>
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