I have a project that is based on SVM algorithm implemented by libsvm <https://www.csie.ntu.edu.tw/~cjlin/libsvm/>. Recently I decided to try several other classification algorithm, this is where scikit-learn <http://scikit-learn.org/> comes to the picture.
The connection to the scikit was pretty straightforward, it supports libsvm format by load_svmlight_file routine. Ans it's svm implementation is based on the same libsvm. When everything was done, I decided to the check the consistence of the results by directly running libsvm and via scikit-learn, and the results were different. Among 18 measures in learning curves, 7 were different, and the difference is located at the small steps of the learning curve. The libsvm results seems much more stable, but scikit-learn results have some drastic fluctuation. The classifiers have exactly the same parameters of course. I tried to check the version of libsvm in scikit-learn implementation, but I din't find it, the only thing I found was libsvm.so file. Currently I am using libsvm 3.21 version, and scikit-learn 0.17.1 version. I wound appreciate any help in addressing this issue. size libsvm scikit-learn 1 0.1336239435355727 0.1336239435355727 2 0.08699516468193455 0.08699516468193455 3 0.32928301642777424 0.2117238289550198 #different 4 0.2835688734876902 0.2835688734876902 5 0.27846766962743097 0.26651875338163966 #different 6 0.2853854654662907 0.18898048915599963 #different 7 0.28196058132165136 0.28196058132165136 8 0.31473956032575623 0.1958710201604552 #different 9 0.33588303670653136 0.2101641630182972 #different 10 0.4075242509025311 0.2997807499800962 #different 15 0.4391771087975972 0.4391771087975972 20 0.3837789445609818 0.2713167833345173 #different 25 0.4252154334940311 0.4252154334940311 30 0.4256407777477492 0.4256407777477492 35 0.45314944605858387 0.45314944605858387 40 0.4278633233755064 0.4278633233755064 45 0.46174762022239796 0.46174762022239796 50 0.45370452524846866 0.45370452524846866
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