# Difference between revisions of "Precision and recall"

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− | '''Precision''' or '''positive predictive value''' is a measure for exactness and '''recall''' or '''sensitivity''' is a measure of completeness. | + | '''Precision''' or '''positive predictive value''' is a [[measure]] for exactness and '''recall''' or '''sensitivity''' is a measure of [[completeness]]. |

They range from 0 to 1 and the best possible value for both of them is 1. | They range from 0 to 1 and the best possible value for both of them is 1. | ||

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:<math>\text{recall} = \frac{tp}{tp + fn},</math> | :<math>\text{recall} = \frac{tp}{tp + fn},</math> | ||

− | :where <math>tp</math> is the number of true positives, <math>fp</math> the number of false positives, and <math>fn</math> the number of false negatives. | + | :where <math>tp</math> is the number of [[true positives]], <math>fp</math> the number of false positives, and <math>fn</math> the number of [[false negatives]]. |

== External links == | == External links == |

## Latest revision as of 10:02, 4 December 2014

**Precision** or **positive predictive value** is a measure for exactness and **recall** or **sensitivity** is a measure of completeness.
They range from 0 to 1 and the best possible value for both of them is 1.

- <math>\text{precision} = \frac{tp}{tp + fp}</math>

- <math>\text{recall} = \frac{tp}{tp + fn},</math>

- where <math>tp</math> is the number of true positives, <math>fp</math> the number of false positives, and <math>fn</math> the number of false negatives.