2013/1/24 Philipp Singer :
> Just another question: If the OVR predicts multiple labels for a sample,
> are they somehow ranked? I know it is just the one vs rest approach, but
> maybe there is some kind of confidence involved. Because then the
> evaluation would be interesting, by looking at ranki
Yep, I know that.
The PR looks promising, will look into it.
Just another question: If the OVR predicts multiple labels for a sample,
are they somehow ranked? I know it is just the one vs rest approach, but
maybe there is some kind of confidence involved. Because then the
evaluation would be i
You should also be aware that the current metrics module doesn't handle
multilabels correctly.
The following pr https://github.com/scikit-learn/scikit-learn/pull/1606
might interest you. It had for multi-labels support for
some metrics.
Best regards,
Arnaud Joly
Le 23/01/2013 18:44, Andreas Muel
Am 23.01.2013 18:39, schrieb Lars Buitinck:
> 2013/1/23 Andreas Mueller :
>> Am 23.01.2013 16:47, schrieb Philipp Singer:
>>> That's what I originally thought, but then I tried it with just using
>>> LinearSVC and it magically worked for my sample dataset, really
>>> interesting. I think it is work
Am 23.01.2013 18:39, schrieb Lars Buitinck:
>
> if you want more predictions or something...
> More in detail: OneVsRestClassifier exports an object called
> label_binarizer_, which is used to transform decision function values
> D back to class labels. By default, it picks all the classes for whic
2013/1/23 Andreas Mueller :
> Am 23.01.2013 16:47, schrieb Philipp Singer:
>> That's what I originally thought, but then I tried it with just using
>> LinearSVC and it magically worked for my sample dataset, really
>> interesting. I think it is working now properly.
> I'm pretty sure it shouldn't.
Am 23.01.2013 16:47, schrieb Philipp Singer:
> Hey,
>
> That's what I originally thought, but then I tried it with just using
> LinearSVC and it magically worked for my sample dataset, really
> interesting. I think it is working now properly.
I'm pretty sure it shouldn't.
> What I am asking myself
* bug for
On Jan 23, 2013 10:48 AM, "Ronnie Ghose" wrote:
> File a bugbor inadequate validation also?
> On Jan 23, 2013 10:34 AM, "Andreas Mueller"
> wrote:
>
>> Hi Philipp.
>> LinearSVC can not cope with multilabel problems.
>> It seems it is not doing enough input validation.
>> You have to us
File a bugbor inadequate validation also?
On Jan 23, 2013 10:34 AM, "Andreas Mueller"
wrote:
> Hi Philipp.
> LinearSVC can not cope with multilabel problems.
> It seems it is not doing enough input validation.
> You have to use OneVsRestClassifier together with LinearSVC
> to do that afaik.
> Che
Hey,
That's what I originally thought, but then I tried it with just using
LinearSVC and it magically worked for my sample dataset, really
interesting. I think it is working now properly.
What I am asking myself is how exactly the decision is made for the
multilabel prediction. Is there some w
Hi Philipp.
LinearSVC can not cope with multilabel problems.
It seems it is not doing enough input validation.
You have to use OneVsRestClassifier together with LinearSVC
to do that afaik.
Cheers,
Andy
Am 23.01.2013 16:27, schrieb Philipp Singer:
> Hey guys!
>
> I am currently trying to do multila
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