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.
Z_SVM example 1.knwf (56.7 KB)
I came across a KNIME video on YouTube (https://www.youtube.com/watch?v=IlqepyIba6Y), but I can’t locate the error. I am using KNIME version 4.1.3.