Hi all, I’m just wondering if anyone has any advice on some ways to deal with evaluating classifier performance on a 4-way problem. I’ve been using the BayesConfusionHypothesis tool which works quite well, but I just wondered what else was out there by way of quantitative evidence to insure that classifier accuracy isn’t being driven by perfect classification between 2 labels, and confusion between the other 2, or whatever. Just glancing at the confusion matrices can give us a good idea about what ROIs are confusing certain conditions, but a more objective solution would be nice. The problem seems to be kinda sidestepped in some of the literature on pattern classification in MRI.
- Gavin ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Gavin Hanson, B.S. Research Assistant Department of Psychology University of Kansas 1415 Jayhawk Blvd., 534 Fraser Hall Lawrence, KS 66045
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