Home
Discussions
Community Q&A
[SOLVED] Select Hyperparameters of SVM in cross-validation?
johnny5550822
Hi,
I want to clarify one thing about what rapidminer exactly is doing. When I put a SVM module inside cross-validation (e.g. 10-fold), will the SVM algoirithm optimize the hyperparameters (e.g. C) based on cross-validation result?
If so, what about if I don't have validation, and basically just give data to SVM module, how does rapidminer get the hyperparameters value?
Thanks,
Johnny
Find more posts tagged with
AI Studio
SVMs
Cross Validation
Accepted answers
All comments
fras
Cross Validation (CV) does not optimize at all. Doing a 10-fold or 5-fold CV ensures only to get performance parameters you can trust.
If you want to optimize e.g. "C" you have to put the CV into the "Optimize Parameters" Operator that performs a training/validating with
all selected values of C. Take a look into the example process delivered together with the operators help.
johnny5550822
Got it, thanks!
Quick Links
All Categories
Recent Discussions
Activity
Unanswered
日本語 (Japanese)
한국어(Korean)