Why we use precision/recall in binary classification but sensitivity(=recall)/specificity in medicine? Sensitivity=recall is used in both fields, but the second metric is different. Why? Both tasks (classification and medicine) look same - data has two classes and we do some predictions on it and want to assess the accuracy.
 A: Sensitivity and specificity have the attractive property that they do not depend on class prevalence - sensitivity is the accuracy among the real positives, while specificity is the accuracy among the real negatives. Since the real positives and real negatives are treated completely separately by these metrics, their relative proportions do not matter. In the medical field, sensitivity/specificity are characteristics of a particular test, completely independent of how many people have the condition being tested for. This makes the statistics of the test invariant in time and place - a test applied to a population with 90% incidence of the condition will have the same sensitivity and specificity when applied to a population with 10% incidence.
Precision, on the other hand, does depend on class prevalence - it is the accuracy among predicted positives, but how many people you predict positive will depend on the prevalence of the condition. If you apply the test to a population with 90% incidence of the condition, you will get one precision value. But when you apply that same test to a population with only 10% incidence, the much larger number of real negatives increases the number of false positives relative to the number of true positives, so the precision will be much lower. As the real positive population diminishes, so too does the probability that a positive test is correct (precision).
Specificity is a characteristic of a test independent of the population it's being applied to, while precision is a characteristic of the test in the specific population it's being applied to. Since condition prevalence can vary by geographic location, or subpopulation, or over time, specificity is usually the preferred means of describing a medical test. As a condition prevalence goes down, the precision of a fixed test goes down, but its specificity does not.
A: Here is the answer:

*

*Both precision/recall pair and sensitivity(=recall)/specificity pair are good metrics and exhaustive enough without the other one.

*But in the medical field, we have omission diagnostic rate and misdiagnosis rate, which is with realistic meaning and may cause huge trouble. Considering this, we use sensitivity (opposite with omission diagnostic rate) and specificity (opposite with misdiagnosis rate).

That's it :)
