Optimization loop start node: Using a logarithmic step size

Hi all,


I am using the loop optimization node with an SVM learner and predictor. The optimization loop asks for a fixed step size. I have a large number of optimizations to do, with a few parameters to adjust in each optimization. Finding the maximum values using a fixed step is not a good idea for me. I have looked up the possible solitions and the machine learning experts suggest the use of a logarithmic scale step size. Is it possible to do this in KNIME?

I have one more question that might be a bit naiive. The SVM leaner loop gives the option to choose the "overlapping penalty", which I could not really find the mathematical/scientific meaning of it. A brief explanation/a reference to what this value is would be very helpful.




Hi Error,

using the optimization loop nodes this is not possible. However, you can also construct a loop your self performing the same straight forward approach as the optimization loop nodes. 

Therefore you first need a list with the parameters and this you use as the configuration table with a table row to variable. Than you are using those parameters to evaluate your svm and finally collect with a loop end node the parameters and achieved accuracy.

Best regards, Iris

There's always the option of using "Java Edit Variable (simple)" to apply arbitrary transformations to the linear steps generated. A "Variable Math Formula" node unfortunately doesn't exist to achieve this with simpler syntax... :-)


Thank you guys for the tips. I will try to construct my own loop as you have suggested. I will have to seek help again in case this did not work for me, if you don't mind :)


The other part of my question was regarding the overlapping penalty, which I couldn't find the proper documentation for, to understand the mathematical meaning of it. If you could point me towards the documentation/ a reference, that would be great.


Best regards,


Hi Error404,

From the node desc:

The overlapping penalty is useful in the case that the input data is not separable. It determines how much penalty is assigned to each point that is misclassified. A good value for it is 1.

It's essentially a symmetric misclassification cost modifier, i.e., it penalises true positives and true negatives equally (or not at all if =0). I'd pragmatically consider it as yet another tuning parameter to cyclce through. :-)


Smells like page 418 (PDF page 437) of the ESL 2nd edition here:


TBC by devs tho.