Hello pyMVPA experts,
I'm relatively new to MVPA, and an issue came up that I'd appreciate
feedback from.
I want to classify based on the visual angle of a stimulus. I have four
different stimulus conditions corresponding to different ways of
presenting the visual angle. I also have theoretical apriori predictions
that classification accuracy should follow condA > condB > condC > condD.
The desire is to get the highest possible classification accuracy
(fairly) for each condition. So, I will run the classification many
times, each time with different classifier parameters (for example, with
a C-SVM I will use different C values).
My question is this: Obviously not all conditions respond to a given C
value in the same way, so different C values are "optimal" for different
conditions. Therefore, is it correct to report the classification
performance for all conditions using the same classifier parameter, or
is it correct to "optimize" each condition's performance independently,
such that each condition potentially uses a different classifier parameter?
I greatly appreciate your thoughts on this question - my gut tells me
that all conditions should use the same parameters, but I can't find a
source that definitively says so.
Thanks again,
Andy
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