"Lower accuracy of libsvm in rapidminer vs weka with same parameters"
Hi,
I am really getting confused. I used a same data set with same parameters in weka and rapidminer for classification with libsvm. the strange result was that weka accuracy for prediction was 15 percent higher than rapidminer???(I used same C and same kernel and same gamma for rbf kernal).I think there is something wrong with weka. it's accuracy on training set for testing phase is 100%.
Can any body explain it?
Thanks a lot.
I am really getting confused. I used a same data set with same parameters in weka and rapidminer for classification with libsvm. the strange result was that weka accuracy for prediction was 15 percent higher than rapidminer???(I used same C and same kernel and same gamma for rbf kernal).I think there is something wrong with weka. it's accuracy on training set for testing phase is 100%.
Can any body explain it?
Thanks a lot.
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There are many different implementations of the SVM. If the Weka implementation works better on your data, you can easily integrate Weka with RapidMiner by installing the Weka Extension. For comparing models please remember to cross-validate them - as you know the training error alone is not an indicator on how well the model will work on unseen data.
Best, Marius
Best, Marius
The version of Weka we implement is v3.6.9. But from this: https://weka.wikispaces.com/LibSVM it appears it's a 3rd party tool that needs to be downloaded and installed seperately. It looks like if you want to use the Weka implementaiton of LibSVM, you have to download it extra.
725 188 211
3 1 1
1 0 1
but weka with same parameters gives
729 0 0
124 65 0
111 0 101
I did not use any weight for classes in weka.Help me please and tell me what is wrong with rapidminer?
thank you.