Do you need probabilities? You could just use the signed distance to each
OVA hyperplane (via ``clf.decision_function()``) to rank the classes. Maybe
the platt-scaling screws up here...
You could also look at Mathieu's "lightning" project
https://github.com/mblondel/lightning - it features multinomial logistic
regression which might give better calibrated probabilities than platt
scaling...
HTH
2013/5/7 Bilal Dadanlar <[email protected]>
> Hi,
>
> For a classification problem, I need a short list of possible classes and
> their confidence of predictions (to find a treshold classifier is 99%
> sure).
>
> I used a multiclass SVM. dataset has 1000 classes, 7-8 instances for each
> and 2000 attributes. *.predict()* results are 72% accurate. However,
> results from *.predict_proba()* didn't work well in this case. most
> probable result is 30% accurate. .predict_proba() works different than
> .predict()
> http://stackoverflow.com/questions/15111408/how-does-sklearn-svm-svcs-function-predict-proba-work-internally
>
> So, is there a way to calculate better predictions for ranking with
> probabilities?
>
> Thank you
>
> --
> Bilal Dadanlar
> cimri.com | Software Engineer
>
>
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their applications. This 200-page book is written by three acclaimed
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