There are six subjects (rows) Class 1 and Class 2 are correct predictions of each class (two values on the diagonal of the confusion matrix). The Total column is the average between them.
On Wed, May 25, 2011 at 3:24 PM, J.A. Etzel <[email protected]> wrote: > I'm missing something here: where are the "class 1" and "class 2" numbers > for each person coming from? > > I agree that describing the results of a multiclass classification is > tricky, especially when using something naturally two-class like svms. > Reporting the multiclass accuracy rate, then the pairwise ones seems most > prudent. > > Jo > > > > On 5/24/2011 2:09 AM, Vadim Axel wrote: > >> Attached an output of one such pathological case (completely real data). >> I even do not talk about splits. It's sufficient that for half subjects >> you have 0.8/0.4 prediction and for other half you have 0.4/0.8... >> >> For more than two classes it really becomes hardly maintainable. I >> recently had 5 classes experiment and I ended up reporting each classes >> separately. >> >> On Mon, May 23, 2011 at 11:01 PM, Yaroslav Halchenko >> <[email protected] <mailto:[email protected]>> wrote: >> >> it is a good thesis indeed, especially for the case of multiclass >> classification where people make claims about unraveling complex >> categorical structure, whenever it is only few categories which get >> "significantly" well classified. >> >> And your illustration goes even further than your verbal description -- >> at first I thought that there is an error, since I expected at least >> one >> class to be significant when "average" accuracy becomes significant. >> But indeed it might be not the case, e.g. if a classifier favors one >> class over another across splits, thus none of the classes come out >> with >> a consistently "significant" performance while mean accuracy does >> (could >> you check if that is indeed the case by looking on per split >> diagonals?). Cool. I always thought that digging in the mud is >> very entertaining ;) >> >> On Mon, 23 May 2011, Vadim Axel wrote: >> >> > Attached an illustration for my thesis. >> > The average classification rate can be considered significant, >> while we >> > clearly see that it is not exactly true... >> >> > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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