Many thanks for the responses thus far!

Did you fix the random seeds across implementations as well?  Differences
in seeds or generators might explain this.

The implementation of libsvm used by Matlab always has a seed of 1. I tried setting the seed for SKL SVM to 1 (and 0, 2, 3, and 4) as well, and the results were still different.

Did you try using the Python API to libsvm directly instead of through SKL?
I'm guessing you have it on your computer since you have the Matlab API.
That would at least let you test whether it's the fake data or whether it's
SKL.

I'll give that a shot next, thanks!

Also are you loading the fake data from a .mat file into Python (e.g. with
the SciPy 'loadmat' function) or are you generating it from a script? Maybe
some weird floating point error between Python and Matlab is giving you the
different results? This could happen if you generate the data with a script
written in both Python and Matlab, for example... along the same lines as
the random seed generator giving different results

I'm generating the fake data with a Python script and saving it to a .txt file, which is then loaded in by Python and Matlab in their respective scripts. To make sure there's no truncation error going on when they load in this .txt file to get the fake data, I applied the floor function to both sets of vectors (to make them ints) in both the Python and Matlab scripts, and they still give different results. So I don't think it's a data issue. 

_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn