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 06:02, 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.
