# Comparing SVM Models using Different Methods for Data Generation

I have a set of SVM models that I am trying to compare. Each of the models is trained on a variation of the original data:

• The original data
• The original data using resampling scheme A
• The original data using resampling scheme B

When I train the models, I use grid search to estimate the SVM hyperparameter $C$ and I get different values under each scheme.

The overall hypothesis that I am trying to test is whether or not resampling scheme B yields a model that performs better than resampling method A.

Is simply comparing performance metrics between scheme B and A acceptable? Or should I be using the same hyperparameters for the models for both schemes?

There is no reason to think the $C$ that you chose should be "universal" and I am not surprised that the best $C$ parameter depends on the sampling scheme you use to fit the model.
With all this in mind, I would feel comfortable using a grid ($C$, sampling scheme) that does not contains too many elements.