Hi Adnan,
probability=True performs a probability calibration on the decision
function.
In order to generate the ROC curve you could directly use the output of
decision_function
method and obtain exactly the same result as if you used probability
calibration (this
is because calibration is a strictly increasing transformation).
Paolo
On Thu, Dec 29, 2011 at 9:46 PM, adnan rajper <[email protected]>wrote:
> hi everybody,
>
> I use LinearSVC for text classification. My problem is that I want to
> generate ROC curve for LinearSVC. Since LinearSVC does not output
> probabilties. Is there any other way to generate ROC curve for LinearSVC?
> I have tried svm.SVC(kernel='linear', probabilities=True) but it gets too
> slow.
>
> Thanks
> Adnan.
>
>
> ------------------------------------------------------------------------------
> Ridiculously easy VDI. With Citrix VDI-in-a-Box, you don't need a complex
> infrastructure or vast IT resources to deliver seamless, secure access to
> virtual desktops. With this all-in-one solution, easily deploy virtual
> desktops for less than the cost of PCs and save 60% on VDI infrastructure
> costs. Try it free! http://p.sf.net/sfu/Citrix-VDIinabox
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
Ridiculously easy VDI. With Citrix VDI-in-a-Box, you don't need a complex
infrastructure or vast IT resources to deliver seamless, secure access to
virtual desktops. With this all-in-one solution, easily deploy virtual
desktops for less than the cost of PCs and save 60% on VDI infrastructure
costs. Try it free! http://p.sf.net/sfu/Citrix-VDIinabox
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
