The specificityis widely used in medicine and consists of the probability of
the diagnostic test finding no disease among those who do not have the disease or
the proportion of people free of disease who have a negative test. More formally,
the specificitySPi of an individual model
i is evaluated by the equation:
where TNi and
FPi represent, respectively, the number of
true negatives and false positives.
True positives (TP), true negatives (TN),
false positives (FP), and false negatives (FN), are the four
different possible outcomes of a single prediction for a
binomial classification task with classes “1” (“yes”) and “0” (“no”). A
false positive is when the outcome is incorrectly classified as “yes” (or “positive”),
when it is in fact “no” (or “negative”). A
false negative is when the outcome is incorrectly classified as negative when
it is in fact positive.
True positives and true negatives are obviously correct classifications.
These four types of classifications are usually shown in a two-way table called the