In pattern recognition, information retrieval, and binary classification, **precision**, also known as **positive predictive value**, is the ratio of true positives, or correctly identified class members, to the total number of positive results or total identified class members. Consider the following table:

Result | |||
---|---|---|---|

Negative | Positive | ||

Actual | Negative | TN | FP |

Positive | FN | TP |

FN (False Negative) ≡ # of actual positive results mis-identified as negative (Type 2 errors)

FP (False Positive) ≡ # of actual negative results identified mis-identified as positive (Type 1 errors)

TP (True Positive) ≡ # of actual positive results identified correctly as positive

The precision is the number of true positive results divided by the number of all positive results:

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