I think SVM does not directly model "Probability" and optimize some
quantity regarding the probability like what logistic regression do. How to
get the probability highly depends on your project(you want high recall or
high precision or some balance over them). In upstream, libsvm does not
seem to support one-class SVM probability output up to now. You can try
doing calibration to the SVM output like isotonic regression
http://scikit-learn.org/dev/modules/isotonic.html.
Best Regards,
Wei LI
On Sun, Jan 20, 2013 at 7:14 PM, hp <jjjjjjl...@163.com> wrote:
> Hello, I am a new user of scikit-learn and I primarily use 2-class SVM
> and 1-class SVM. Now, I meet with a problem and hope someone can give me a
> reply.
>
> I want the 1-class SVM probability output just like the function 2-class
> SVM has provided. I know that scikit-learn implementation is based on
> libsvm. So I directly transfer 1-class SVM probability estimate function to
> 1-class SVM. But the probability outputs are not correct. I have two
> questions: (1) whether 1-class SVM can output probability estimate ? If can
> (2) whether the present probability estimate algorithm supports 1-class SVM?
>
> In my initial opinion, 1-class SVM is to generate the hyperplan bewteen
> origin point and trainning data. Both 1-class and 2-class SVM are not
> completely different.
>
> Sorry for my poor judgment. I just begin to study and use the SVM machine
> learning method.
>
> Best wish for everyone
> ------
> YS.JI
>
>
>
>
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--
LI, Wei
Tsinghua/CUHK
http://kuantkid.github.com/
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