How to combine Parameter Optimization and Cross Validation


I’d like to use a parameter optimization loop on top of cross validation but I can’t seem to get it to work in a workflow. Basically, I’m trying to run a series of parameter combinations each through a 10-fold cross validation and return the optimal set of parameters. Any suggestions? Not sure if this is already covered in an example…


Hi macsmith,

Please find attached an example of training an SVM model using a 5-fold cross validation combined with parameter optimisation.

Cross_Validation_with_SVM_and_Parameter_Optimisation.knar.knwf (53.7 KB)


So simple. Thanks for this. I was running into trouble because my cross-validation was contained inside a metanode and I was having difficulties connecting the start and end loops through the metanode ports (and i think i had the scorer out of place).

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