# c-classification SVM vs nu-classification SVM in e1071 R

When should I use c-svm and when nu-svm? I have read that "The range of C is from zero to infinity, but nu is always between [0,1]", but I couldn't understand anything from this.

What is the range here?

If I have a binary classification with all my dependents being numerics what should I prefer? If I have multinominal classification with mixed independent variables what should I prefer?

• They are equivalent. $\nu$ can be expressed as a function of $C$, see here. Nov 9 '17 at 18:32
• @Firebug I don;t have python knowledge couldn't follow the C that they are talking about. In R there is a cost and gamma parameters. I will try to read some more material on this. - Thanks for your help Nov 13 '17 at 10:16
• That question is not about python (it's only tagged due to scikit-learn). Read the first answer. Nov 13 '17 at 10:19

I once asked a question very similar to your: Difference between the types of SVM.

Here is the relevant part of the answer.

C-classification and nu-classification is for binary classification usage. Say if you want to build a model to classify cat vs. dog based on features for animals, i.e., prediction target is a discrete variable/label.

For details about difference between C-classification and nu-classification. You can find in the FAQ from LIBSVM

Q: What is the difference between nu-SVC and C-SVC?

Basically they are the same thing, but with different parameters. The range of C is from zero to infinity but nu is always between [0,1]. A nice property of nu is that it is related to the ratio of support vectors and the ratio of the training error.