Cross-Validation returns different model
Hello,
I am using RapidMiner Studio 7.2.002 and recognized something strange after training libSVM (nu-SVC, linear kernel) classifier within X-Validation and outside X-Validation (with the same parameters): the output models are different! According to the documentation of the X-Validation operator the output model is trained on the whole example set, which would be the same as just using the libSVM training.
Do I miss anything or why are these models different although the operators, parameter and data are the same?
Best
Mark
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OPHi Martin,
I don't use any preprocessing inside the X-Validation: only libSVM, Apply Model and Performance Classification (Accuracy). The data and parameters are the same.
Best
Mark
Hi Mark,
the only way i can imagine that this one happens is that their is a randomness in the algorithm or the starting points of the optimization make a difference. I've tested it on sonar and saw no difference - but that might not be that meaningful.
Are you doing some kind of preprocessing inside x-val which might be different?
~martin