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POSVM implmentation, scikits learn reducing runtime, fastest svm
 

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    <p>I'm working with scikit-learn on building some predictive models with SVMs. I have a dataset with around 5000 examples and about 700 features. I'm 5 fold cross validating with a 18x17 grid search on my training set then using the optimal parameters for my test set. the runs are taking a lot longer than I expected and I have noticed the following:</p> <p>1) Some individual SVM training iterations seem to take only a minute, while others can take up to 15 minutes. Is this expected with different data and parameters (C and gamma, I'm using <code>rbf</code> kernel)?</p> <p>2) I'm trying to use 64 bit python on windows to take advantage of the extra memory, but all my python processes seem to top at 1 gig in my task manager, I don't know if that has anything to do with the runtime.</p> <p>3) I was using 32bit before and running on about the same dataset, and i remember (though I didn't save down the results) it being quite a bit faster. I used a third party build of scikit-learn for 64 bit windows, so I don't know if it's better to try this on 32 bit python? (source <a href="http://www.lfd.uci.edu/~gohlke/pythonlibs/" rel="nofollow">http://www.lfd.uci.edu/~gohlke/pythonlibs/</a>)</p> <p>Any suggestions on how I can reduce runtime would be greatly appreciated. I guess reducing the search space of my grid search will help but as I'm unsure of even the range of optimal parameters, I'd like to keep it as large as i can. If there are faster SVM implementations as well, please let me know, and I may try those.</p> <p>Addendum: I went back and tried running the 32bit version again. It's much faster for some reason. It took about 3 hours to get to where the 64bit version got to in 16 hours. Why would there be such a difference?</p>
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