I don't think its a trick. Its simply agreeing on a definition of accuracy in a multiple decision framework. I mean, you could have a accuracy and precision value for each class. Simply label "positive" as being for that class and "negative" for being anything but that class. Other than that, I don't know how else to extend that definition to multiple classes.
On Jun 2, 2009 3:45pm, Carlo Rossi <address@hidden> wrote: > > > mmm it's actutally possible using some trick. > > Sincerely matrix C it's equal to cp.CountingMatrix (that contains the confusion matrix). So basically I should work on the same matrix. > > Sincerely again, the cp.CountingMatrix is slightly different: > > http://www.mathworks.com/access/helpdesk/help/toolbox/bioinfo/index.html?/access/helpdesk/help/toolbox/bioinfo/ref/classperf.html > > it has a line at the end for Nan cases. > > > > I hope somebody here have experience and to let me know which is the right accuracy > > Actaully I didn't understand your point of view on that.. > > thanks, > > > > > I'm not familiar with these > > > particular functions, but I find it slightly odd that > > > you're using terms/statistics for a binary decision in a > > > multiple decision framework. > > > > > > That said, acc2 is something akin to the True Positive Rate > > > and I wouldn't expect it to be the same as acc1 unless > > > there is some definition that extends ideas like accuracy to > > > a multiple decision framework. > > > > > > > > > On Tue, Jun 2, 2009 at 2:22 PM, > > > Carlo Rossi address@hidden> > > > wrote: > > > > > > > > > > > > Hello, > > > > > > it isn't obvious because implementing it (but with > > > Matlab in this two way: > > > > > > > > > > > > classification = knnclassify(TEST, TRAIN, GROUP, 1); > > > > > > [C, order] = confusionmat(TARGET, classification); > > > > > > cp = classperf(TARGET, Kclassification); > > > > > > acc1 = > > > (cp.Sensitivity*cp.Prevalence)cp.Specificity*(1-cp.Prevalence) > > > > > > acc2 = sum(diag( C )) / sum( C(:) ) > > > > > > > > > > > > According to here I should return the same accuracy: > > > > > > http://en.wikipedia.org/wiki/Accuracy_and_precision > > > > > > > > > > > > But they are diffent! So for this reason I asked If > > > I were using the right formula. Does anyone have > > > experience with this stuff? > > > > > > I need to understand why the are different > > > > > > thanks, > > > > > > > > > > > > --- Mar 2/6/09, Jaroslav Hajek address@hidden> > > > ha scritto: > > > > > > > > > > > > > Da: Jaroslav Hajek address@hidden> > > > > > > > Oggetto: Re: accuracy on a matrix > > > > > > > A: "Carlo Rossi" address@hidden> > > > > > > > Cc: address@hidden > > > > > > > Data: Martedì 2 giugno 2009, 07:14 > > > > > > > On Tue, Jun 2, 2009 at > > > 2:40 AM, Carlo > > > > > > > Rossi address@hidden> > > > > > > > wrote: > > > > > > > > Hello, > > > > > > > > I have a problem that is not strictly on Octave > > > but > > > > > > > maybe it can be > > > > > > > > interesting as I didn't find solution > > > anywhere. > > > > > > > > I have a matrix where each column/rows represent > > > a > > > > > > > class; I'm speaking about > > > > > > > > a confusion matrix. > > > > > > > > for example, three classes conf. matrix > > > > > > > > A = [2 1 1; 0 3 1; 0 0 4]; > > > > > > > > > > > > > > > > and I read this: http://en.wikipedia.org/wiki/Accuracy_and_precision > > > > > > > > Is there any chance to use the first formula of > > > > > > > accuracy (actually with more > > > > > > > > than 2 classes I don't understand how apply > > > it) > > > > > > > without use the > > > > > > > > Prevalence,Sensitivity etc? > > > > > > > > > > > > > > > > thanks, > > > > > > > > > > > > > > > > > > > > > > It's obvious, isn't it? > > > > > > > accuracy = trace(A) / sum(A(:)); > > > > > > > Diagonal elements represent correct classifications, > > > the > > > > > > > rest are > > > > > > > misclassifications. > > > > > > > > > > > > > > cheers > > > > > > > > > > > > > > -- > > > > > > > RNDr. Jaroslav Hajek > > > > > > > computing expert & GNU Octave developer > > > > > > > Aeronautical Research and Test Institute (VZLU) > > > > > > > Prague, Czech Republic > > > > > > > url: www.highegg.matfyz..cz > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > Help-octave mailing list > > > > > > address@hidden > > > > > > https://www-old.cae.wisc.edu/mailman/listinfo/help-octave > > > > > > > > > > > > > > > > > > > > >