The positive predictive value is widely used in medicine and consists of the percentage
of people with a positive diagnostic test who actually have the disease. More formally,
the positive predictive valuePPVi of an individual model
i is evaluated by the equation:
where TPi and FPi represent, respectively, the number of true
positives 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