Binary classification

Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Typical binary classification problems include:

Medical testing to determine if a patient has a certain disease or not;
Quality control in industry, deciding whether a specification has been met;
In information retrieval, deciding whether a page should be in the result set of a search or not
In administration, deciding whether someone should be issued with a driving licence or not
In cognition, deciding whether an object is food or not food.

When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a false positive) is considered differently from not detecting a disease when it is present (a false negative).