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Jul 15, 2014 at 16:39 comment added yaromir And one more minor question. Shouldn't there be $P(TP)_1(FN)_2(TP)_3>(1-P)(FP)_1(TN)_2(FP)_3$ instead of $P(TP)_1(FN)_2(TP)_3>(1-P)(TP)_1(FN)_2(TP)_3$ ? If i understood correctly, left part represents probability that answer that was given by majority of NNs is correct while right part is a probability that despite that majority of the NNs gave this answer this major answer is wrong (false answer of the whole system).
Jul 6, 2014 at 0:41 comment added fedja @yaromir The classical names for the two independent parameters are sensitivity and specificity, but to be honest, I never remember what exactly those are and have to google them every time. The point however is that the error of saying "yes" when the answer is "no" may be very different from the error of saying "no" when the answer is "yes" in terms of both the probability and the cost of erring. So I just decided to cover more than you formally asked for.
Jul 6, 2014 at 0:36 comment added fedja In general, there are 4 probabilities for any testing device with y/n output: true positive (probability to detect a present thing (threat, disease, dependence, whatever), true negative (not detecting an absent thing), false positive (detecting something that is not there) and false negative (failing to detect something that is there). Ideally, the device should have tp=tn=1, fp=fn=0, but that never happens. There are two obvious relations: tp+fn=tn+fp=1, but otherwise 4 numbers are independent. I call the whole set of these values "detection table", though it is not a standard name
Jul 5, 2014 at 23:22 comment added yaromir Huge thanks. It helped a lot. Can i ask you to give a bit more explanation about detection tables? I'm sorry, i didn't get the point. Thanks.
Jul 5, 2014 at 23:21 vote accept yaromir
Jul 5, 2014 at 5:11 history answered fedja CC BY-SA 3.0