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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