>No. If there were, we would have implemented predict_proba.
One of my concern is sklearn.neighbors.NearestCentroid .
The algorithm "nearest shrunken centroids" applied in R (called pamr)
provide "posterior probabilities" while sklearn.neighbors.NearestCentroid
is missing this feature.
On 28 July 2014 23:46, Mario Michael Krell <[email protected]> wrote:
> 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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