SciKit Learn SVR runs very long

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I'm facing the following problem, I'm running a SVR from the scikit-learn library on a training set with about 46500 obsevations and it runs more than six hours, until now.

I'm using the linear kernel.

def build_linear(self):
    model = SVR(kernel='linear', C=1)
    return model

I already tried changing the "C" value between 1e-3 and 1000 nothing changes.

The poly kernel runs in about 5 minutes, but I need the values for an evaluation and can skip this part...

Does anyone got an idea how to speed this up?

Thanks a lot!

scikit-learn svm machine-learning python
1 Answers

SVMs are known to scale badly with the number of samples!

Instead of SVR with a linear-kernel, use LinearSVR or for huge data: SGDClassifier

LinearSVR is more restricted in terms of what it can compute (no non-linear kernels) and more restricted algorithms usually have more assumptions and use these to speed-up things (or save memory).

SVR is based on libsvm, while LinearSVR is based on liblinear. Both are well-tested high-quality implementations.

(It might be valuable to add: don't waste time in general cases like these waiting 6 hours. Sub-sample your data and try smaller, less small, ... examples and deduce runtime or problems from that. edit: it seems you did that already, good!).

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