[scikit-learn] Multiple normal scenario for OCSVM

[Albert Thomas]

Hi Ady,

Overfitting is a possible explanation. If your model learnt your normal
scenarios too well then every abnormal data will be predicted as abnormal
(so you will have a good performance  for anomalies) however none of the
normal instances of the test set will be in the normal region (so you will
have a high FPR).

Albert

On Wed, 5 Apr 2017 at 15:37, Ady Wahyudi Paundu <awpaundu at gmail.com> wrote:

> Good day Scikit-Learn Masters,
>
> I have used Scikit-Learns OCSVM module previously with satisfying results.
> However on my current tasks I have this problem for one-class analysis:
>
> In my previous cases, I used OCSVM for Anomaly detector, and the
> normal classes in each cases were coming from one scenario.
> Now, I want to create one Anomaly detector system, with multiple
> normal scenario (in this case, 3 different normal scenario). Lets say
> I have scenario A, B and C, and I want to distinguish all data that is
> not coming from A and B and C.
> What I have been tried is combining all training data A and B and C
> into one data set and fit it using OCSVM module. When I tested the
> output model to several anomaly data-set it worked good. However, when
> I tested it against either one of the normal scenario, it gave a very
> high False Positives (AUROC: 99%).
>
> So my question, is it because a bad approach? by combining all the
> different normal data set into one training data set.
> Or is it because I was using it (the OCSVM) wrong? (i use 'rbf' kernel
> with nu and gamma set to 0.001)
> Or is it the case with wrong tools? another algorithm perhaps?
>
> I dont know if this is a proper question to ask here, so if it is not
> (maybe because this is just a Machine Learning question in general),
> just disregard it.
>
> Thank you in advance
>
> Best regards,
> Ady
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20170405/6fe4335c/attachment-0001.html>