I am using SVM for binary classification in the setting of more predictors (367) than samples (in the order of 200). I have normalized my predictors, all quantitative, but I do not want to use PCA to ease interpretation, and I used a high correlation filter instead (workflow attached, but cannot upload with data due to excessive weight). I want to tune hyperparameters (cost and sigma), and I have used 2 SVM configurations. In the upper one, I used the SVM Learner, and in the lower one I used the LIBSVMLearner instead. Specific questions:
In the SVMLearner, is the “Overlapping penalty” equivalent to the “Cost” parameter?
With both, I have problems configuring the flow variables (Cost and Sigma) in the Parameter Optimization Loop Start node. How can I do it properly? The flow variables do not appear in the Flow variable section of the Learner.
As best I can tell, the Overlapping penalty is equivalent to the cost function.
As for some of your other problems, I think the failure of the LIBSVM Learner node stems from an inconsistent looping setup between the top branch and the bottom branch. Second, you need to make sure you are applying flow variables and not creating new ones in the Learner:
Awesome. I’ve been using KNIME for years and am now just learning what those mysterious empty text fields do – they create new downsteam flowvariables. Thanks!
