Hi,
I have recently used partial least squares(PLS) and kernel partial least
squares (KPLS) for a similar task, where I was interested in prediction
confidence for each class on test data. It worked for me better than *SVM*and
*predict_proba()*. PLS is already implemented in sklearn but not KPLS. To
use PLS for this task you have treat your problem as a regression problem
where the target matrix encodes class memberships of each training
samples(read chapter 4 from the Elements of statistical learning).
Hope that this help.
A.Eweiwi
On Tue, May 7, 2013 at 11:03 AM, Peter Prettenhofer <
[email protected]> wrote:
> 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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>
>
> --
> Peter Prettenhofer
>
>
> ------------------------------------------------------------------------------
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> leaders in the field. The early access version is available now.
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their applications. This 200-page book is written by three acclaimed
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