That isn't bounded, but 1 / (1 + dist) would be. exp(-dist / c) would
probably get too small too quickly.
On Thu, Jul 31, 2014 at 2:09 PM, Lars Buitinck wrote:
> 2014-07-31 14:04 GMT+02:00 Sheila the angel :
> > Also the NearestCentroid classifier do not have decision_function !
>
> I think we
2014-07-31 14:04 GMT+02:00 Sheila the angel :
> Also the NearestCentroid classifier do not have decision_function !
I think we should add one, but I've never bothered to figure out what
the right decision function would be. Inverse of distance?
Also the NearestCentroid classifier do not have decision_function !
>In some cases, you can get more information from
classifier.decision_function().
On 28 July 2014 20:43, Josh Vredevoogd wrote:
> In some cases, you can get more information from
> classifier.decision_function(). The output w
2014-07-29 13:55 GMT+02:00 Sheila the angel :
>>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.
Here: https://github.com/scikit-learn/scikit-learn/pull/1176
On 29 July 2014 21:59, Lars Buitinck wrote:
> 2014-07-28 23:46 GMT+02:00 Mario Michael Krell :
> > 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.
> >
2014-07-28 23:46 GMT+02:00 Mario Michael Krell :
> 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 integrate
>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 Jul
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 sc
In some cases, you can get more information from
classifier.decision_function(). The output will not be a probability but
can still be more useful than the binary result -- I'm thinking of
meta-classifiers or classifier evaluation. Caveat: there are likely gotchas
in going this direction if you don
2014-07-28 18:39 GMT+02:00 Sheila the angel :
> 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_
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?
Thank you,
--
Infragistics Pr
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