I believe this message is from Abhi too - regarding the same question:
How can we combine probabilities from multiple classifiers in sklearn?
> [Classifiers are trained on similar type datasets, difference being their
> sizes and the way each result might be used]. I am using SGDClassifier to
> train the individual classifiers, and need to choose the best amongst them.
> But as I understand I would need to normalize first before comparing
> them and was not sure how to calibrate them as such. Any pointers
> to would be helpful.
2013/11/26 Olivier Grisel <[email protected]>
> 2013/11/26 Abhi <[email protected]>:
> > How can we normalize and compare probabilities from different classifier
> > models in scikit?
>
> Why do you want to do that?
> To be more specific, how do you quantify success of your task?
>
> By definition, probabilities are "normalized" in the sense that the
> are guaranteed to live in the [0-1] range. However the classifier
> models can be predict arbitrarily bad probabilities. For instance a
> badly trained or badly parameterized binary classifier could predict 0
> proba for the positive class 100% of the time.
>
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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