If I have values for `sensitivity`

and `specificity`

for a group of studies, for example like this:

`sensitivity specificity ----------- ----------- 66.3 74.7 87.2 65.9 56.4 76.4 79.5 94.3 `

How can I make an `ROC`

curve for such values? Do I divide them by `100`

for example and just put the values in the curve straightforward? Or, how exactly does the process work out?

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#### Best Answer

You seem to have several readings of $text{sensitivity}$ and $text{specificity}$ for some classifier; yet ROC curve does not work that way. It is a way of plotting the performance of a learner giving some numerical confidence that some object is of a class A and not B instead of certain prediction (i.e. this object is A, that is B).

Obviously such confidence-score-giving learner can be converted into a plain two class classifier by defining some threshold $t$ and saying that it votes for class A if the $text{score}>t$ and for B otherwise. And, for this classifier, one can get $text{sensitivity}$ and $text{specificity}$.

Thus, for such learner, you can get two functions: $text{sensitivity}(t)$ and $text{specificity}(t)$; ROC is a visualization of those two functions as a parametric curve $(1-text{specificity}(t),text{sensitivity}(t))$. In practice $t$ has finite number of unique values (at most equal to number of objects), so the curve is constructed from points for all of them.