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.
[1] H.-T. Lin, C.-J. Lin, and R. C. Weng, “A note on Platt’s probabilistic
outputs for support vector machines,” Mach. Learn., vol. 68, no. 3, pp.
267–276, Aug. 2007.
http://pyspace.github.io/pyspace/api/generated/pySPACE.missions.nodes.postprocessing.score_transformation.html?highlight=psf#plattssigmoidfitnode
>
> 2014-07-28 18:39 GMT+02:00 Sheila the angel <[email protected]>:
>> For the classifier which do not provide probability estimate of the class
>> (gives error 'object has no attribute predict_proba " ), is there any easy
>> way to calculate the posterior probability?
>
> No. If there were, we would have implemented predict_proba.
>
> (Or yes, but it's always zero or one.)
>
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