Re: accuracy on a matrix

[Carlo Rossi]

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
> 
> >
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
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