I'm a new contributor to this repo, could I work on this?

Hey @VathsalaAchar. Sure, go for it.
Make sure you include a regression test that ensures the correct behavior.

I am new contributor and I am trying to learn my way around the code.

I think I might have found the problem, in svm.cpp. Although the labels are 'int'
model->label = Malloc(int,nr_class);
in the function predict the value is cast into a double:
double PREFIX(predict_values)(..){ ... return model->label[vote_max_idx];}
I believe that in order to fix that the function definition and all top level calls would have to changed to int.

Does it seem that I am on the right track?

@mppaskov I don't think so. I think that we can just overide the predict function and change the type to int.
Because that is how BaseSVC do the job. You can find these lines in predict function:

y = super(BaseSVC, self).predict(X)
return self.classes_.take(np.asarray(y, dtype=np.intp))

BaseSVC's predict function use the predict from BaseLibSVM, and BaseLibSVM is the only super class of OneClassSVM.

@REM2016 Thank you for clarifying, I think went too deep into the rabbit hole. I was a bit skeptical of changing the underlying API calls in libsvm.
But essentially just applying the same fix from BaseSVC to OneClassSVM. I also believe that this is exactly what @VathsalaAchar implemented in her PR.

@amueller I made the changes and the PR has been merged, just letting you know in case you want to close this issue.

Thanks @VathsalaAchar !