Is it logical that testing error be lower than training error?
njasaj
New Altair Community Member
Hi Rapidminer Community,
I used SVM (Libsvm) operator for making a regression model. After training by 10 fold cross validation the resulted correlation coefficient was 84 and RMSE was 0.048. By applying this model on the test data set i got correlation coefficient of 88.5 and RMSE of 0.037. Now i need to know is it possible or logical that testing error be lower than training error?
Thanks.
I used SVM (Libsvm) operator for making a regression model. After training by 10 fold cross validation the resulted correlation coefficient was 84 and RMSE was 0.048. By applying this model on the test data set i got correlation coefficient of 88.5 and RMSE of 0.037. Now i need to know is it possible or logical that testing error be lower than training error?
Thanks.
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Answers
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Hi,
yes, this is possible. You have to keep in mind that using only one testset does
_not_ deliver representative results. That's why we have Cross-Validation where
averaging over more than one testset is done. So trust Cross-Validation for choosing the
right SVM-parameters and finally train your model on full data.
Cheers, Frank0 -
Thanks for your reply. Would you mind explain a bit more about this? I think that cross validation is only for train data set and after finding the model parameters just apply the model on test data set. As i understand you recommend use cross validation on test data ? So what happen for the other part of data which was splitted by CV for training?
Thank you.0