Re: accuracy on a matrix

[Carlo Rossi]

Hello,
 I assume then the right formula is:
acc2 = sum(diag( C )) / sum( C(:) )
But what I was looking for is a formula or a way to have confirm on my result. 
is there any?
thanks,

--- Mer 3/6/09, 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: Mercoledì 3 giugno 2009, 06:59
> On Tue, Jun 2, 2009 at 9:49 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,
> >> >
> >>
> >> The acc1 formula above is only valid for binary
> >> classification and
> >> requires splitting the class labels into positive
> and
> >> negative. See
> >> the documentation of classperf, "PositiveValue"
> and
> >> "NegativeValue".
> >
> > sorry you finished into my antispam! Then, only acc1
> formula is right for only binary or even the acc2. Actually
> I need one that can work on multiple classes...
> > thanksm
> >
> 
> Accuracy is defined as the ratio of (correctly classified
> cases) /
> (all cases), which is naturally extended to multiple
> classes and given
> by the simple formula acc2.
> However, given the definitions of Sensitivity, Prevalence
> and
> Specificity, I don't see a similarly natural way to extend
> them to
> multiple classes. Matlab apparently does neither; instead
> it
> partitions the class labels into two groups and then
> applies the
> binary formulas.
> 
> 
> -- 
> RNDr. Jaroslav Hajek
> computing expert & GNU Octave developer
> Aeronautical Research and Test Institute (VZLU)
> Prague, Czech Republic
> url: www.highegg.matfyz.cz
>