Hi,
Your question got my attention as I was just reading today some CVPR'12
about how to calibrate the scores of the SVM classifiers into a probability
distribution using the Extreme Value Theory.
The authors do provide the code wrapped into python using SWIG, tried to
hack it today but did not yet succeed, probably should explore it further.
Give it a try!
Best,
A.Eweiwi
On Mon, Aug 13, 2012 at 9:49 PM, Andreas Mueller
<[email protected]>wrote:
> On 08/13/2012 08:29 PM, Abhi wrote:
> > Andreas Müller <amueller@...> writes:
> >
> >> Alternatively you could look at the output of "decision_function" in
> > LinearSVC.
> >> These do not represent probabilities, though.
> >>
> >> Andy
> >>
> >
> > Hi Andy, thanks for pointing me towards that. I looked around online but
> I'm
> > still not sure how I can use the decision_function method to determine
> how good
> > the match was (i.e. how confident LinearSVC's prediction that the
> input is in
> > this category is). Could you shed some light on this?
> >
> The problem with the values are that they are not normalized, so the
> range is
> hard to interpret. If you have a two-class problem, higher means more
> confident for
> positive class and lower means more confident for negative class.
> This value can be used for example to plot roc-curves and do
> precision-recall trade offs.
>
> I am using "confident" here in an informal way.
>
> If you want real probabilities, try LogisticRegression, as I said in my
> other mail.
>
> Cheers,
> Andy
>
>
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