What's the difference between using libSVM in sci-kit learn, or e1070 in R, for training and using support vector machines?
Sometime back I had the same question. Yes, both e1070 and scikit-learn use LIBSVM. I have experience with e1070 only.
But there are some areas where R is better. I have read in the past that Python does not handle categorical features properly (at least not right out of the box). This could be a big deal for some.
I also prefer R's formula interface. And some of the nice data manipulation packages.
Python is definitely better for general purpose programming and scikit-learn aids in using a single programming language for all tasks.
Recently I was contemplating the choice of using either R or Python to train support vector machines.
Aside from the particular strengths and weaknesses intrinsic to both programming languages, I'm wondering if there is any heuristic guidelines for making a decision on which way to go, based on the packages themselves.
I'm thinking in terms of speed of training a model, scalability, availability of different kernels, and other such performance-related aspects.
Given some data sets of different sizes, how could one decide which path to take?  
I apologize in advance for such a possibly vague question.