[BioC] what metric is used to evalute the error rate?

[Kevin R. Coombes]

Shouldn't there be error bars (i.e., confidence intervals) around those 
error estimates, that ought to get smaller when you use more samples?

Best,
	Kevin

Weiwei Shi wrote:
> Hi, there:
> 
> This is a question a little bit off topic but I believe many people
> using bioconductor might have this situation so I ask it here and hope
> I can get some suggestion.
> 
> I have a result which looks like this:
>      net num.genes overall.error overall.pred.error
> 1 custom         5       0.15625 0.05263
> 
> The overall.error is (b+c)/(a+b+c+d) from cross-validation for
> training data; while the overall.pred.error is the one for test data.
> Since the sample sizes of training and test data are different, it
> gives me the result which performs better in test than training. I am
> wondering if there are some other metrics to evalute this
> classification error rate so that it can consider the effects of
> sample size.
> 
> thanks
>