Thank you all. I havent thought of regression before. but i'll give it a
try.
afaik, platt scaling also use .decision_function() values, which gives
N*(N-1)/2 signed distance for each class pair (Ni,Nj).
but I'm not sure if i can create a better ranking according to them.
On Tue, May 7, 2013 at 1:09 PM, Peter Prettenhofer <
[email protected]> wrote:
>
>
>
> 2013/5/7 Lars Buitinck <[email protected]>
>
>> 2013/5/7 Peter Prettenhofer <[email protected]>:
>> > 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...
>>
>> The more I find out about Platt scaling in LibSVM, the more I'm
>> inclined to stay away from it.
>>
>> > 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...
>>
>> Or our own LogisticRegression. It cuts some corners, but sometimes
>> it's good enough.
>>
>
> Right, it should give you the same ordering as ``decision_function`` (just
> normalized).
>
>
>>
>> --
>> Lars Buitinck
>> Scientific programmer, ILPS
>> University of Amsterdam
>>
>>
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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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--
Bilal Dadanlar
cimri.com | Yazılım Mühendisi
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"Graph Databases" is the definitive new guide to graph databases and
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