NuSVR vs SVR in scikit-learn

Question

sklearn provides two SVM based regression, SVR and NuSVR. The latter claims to be using libsvm. However, other than that I don't see any description of when to use what. Does anyone have an idea? I am trying to do regression on 3m X 21 matrix using 5 fold cross validation using SVR, but it is taking forever to finish. I've aborted the job and I'm now considering using NuSVR. But I'm not sure what advantage it provides.

NuSVR - http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html#sklearn.svm.NuSVR SVR - http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR

Answer 1

They are equivalent but slightly different parametrizations of the same implementation. Most people use SVR. You can not use that many samples with a kernel SVR. You could try SVR(kernel="Linear") but that would probably also be infeasible. I recommend using SGDRegressor. You might need to adjust the learning rate and number of epochs, though.

You can also try RandomForestRegressor which should work just fine.

Answer 2

Check out the github code for nuSVR. It says that it is based on libSVM as well. NuSVR allows you to limit the number of support vectors used.