accuracy on a matrix

[James Sherman Jr.]

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 <serosole at yahoo.it> 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 <highegg at gmail.com> ha scritto:
>
> > Da: Jaroslav Hajek <highegg at gmail.com>
> > Oggetto: Re: accuracy on a matrix
> > A: "Carlo Rossi" <serosole at yahoo.it>
> > Cc: help-octave at octave.org
> > Data: Martedì 2 giugno 2009, 07:14
> > On Tue, Jun 2, 2009 at 2:40 AM, Carlo
> > Rossi <serosole at yahoo.it>
> > 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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