Editing Binary classification (section)

{{Short description|Dividing things between two categories}}
{{More citations needed|date=May 2011}}
'''Binary classification''' is the task of [[classification|classifying]] the elements of a [[Set (mathematics)|set]] into one of two groups (each called ''class''). Typical binary classification problems include:
* [[Medical test]]ing 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 (government)|administration]], deciding whether someone should be issued with a driving licence or not
* In [[cognitive categorization|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 [[false positive and false negative|types of errors]] is of interest. For example, in medical testing, detecting a disease when it is not present (a ''[[false positives and false negatives#False positive error|false positive]]'') is considered differently from not detecting a disease when it is present (a ''[[false positives and false negatives#False negative error|false negative]]'').

[[Image:binary-classification-labeled.svg|thumb|220px|right|In this set of tested instances, the instances left of the divider have the condition being tested; the right half do not. The oval bounds those instances that a test algorithm classifies as having the condition. The green areas highlight the instances that the test algorithm correctly classified. Labels refer to: <br />TP=true positive; TN=true negative; FP=false positive (type I error); FN=false negative (type II error); TPR=set of instances to determine true positive rate; FPR=set of instances to determine false positive rate; PPV=positive predictive value; NPV=negative predictive value.]]