The linked article contains exactly this information, though. Figure 1 showed the Receiver Operating Characteristic curve, which they called something else, but it's the standard way of discussing this. It plots the classifier's true positive rate versus the false positive rate at various settings thusly: the y-axis plots the "sensitivity", which is the true positive rate. The x-axis plots the "specificity", which is equal to (1 - false positive rate)... but because everyone is thinking of the false positive rate anyway, you'll notice that the specificity goes from 1 to 0.

Incidentally, these are plotted as a curve because it's trivial to build a classifier with a 100% true positive rate (call them all suicidal) or a 0% false positive rate (call none of them suicidal), but just how "good" your model / algorithm is is a function of how it performs with both.