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Let's consider a binary supervised classification problem. Be "A" and "B" the two classes. Sometimes it is said that it if an individual belongs to one of the two classes, we have a "positive event" while if he belongs to the other class we have a "negative event". I know that what is a "positive event" and what is a "negative event" depends on the specific problem but I don't understand how to recognize them. Can you help me? Thank you.

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    $\begingroup$ What do you mean by recognizing them? positive and negative are just two names you give to the classes. You can name them plus and minus or zero and one. $\endgroup$ – seteropere Aug 22 '15 at 17:34
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As you've said it depends on the problem's definition. So, if you read a paper it has to be stated somewhere what the positive class is. Maybe read up on how a confusion matrix is build and try out the R-function confusionMatrix that comes with the caret package. Said function lets you define what the positive class is and you can see how the evaluation measures change.

library(caret)

lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
                levels = rev(lvs))
pred <- factor(
  c(
    rep(lvs, times = c(54, 32)),
    rep(lvs, times = c(27, 231))),               
  levels = rev(lvs))

xtab <- table(pred, truth)

confusionMatrix(xtab, positive = "abnormal")
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