Tool to compute statistical measures of Precision and Recall. Precision and recall are two statistical measures which can evaluate sets of items.

Precision and Recall - dCode

Tag(s) : Data Processing, Statistics

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The **precision and the recall** are two statistical values which make it possible to characterize the differences between 2 sets of elements: the calculated/selected set (to be evaluated/compared) and the expected set (reference/gold standard).

Precision is the ratio of the number of common elements relative to the size of the calculated set. Precision is also known as *positive predictive value*.

The reminder is the ratio of the number of common elements relative to the size of the expected set. The recall is also known as *true positive rate* or *sensitivity*.

For more statistical data, see the Confusion Matrix page.

For a search, the precision is the ratio of the number of pertinent items found over the total number of items found.

$$ \text{Precision}=\frac{|\{\text{Relevant items}\}\cap\{\text{Retrieved items}\}|}{|\{\text{Retrieved items}\}|} $$

__Example:__ The expected (reference) set is `A,B,C,D,E` (5 items) and the retrieved/found set are `B,C,D,F` (4 items). The set of expected items retrieved is `B,C,D` (3 common items). The precision is $$ P = \frac{3}{4} = 75\% $$

The recall is the ratio of the number of pertinent items found over the total number of relevant items.

$$ \text{Recall}=\frac{|\{\text{Relevant items}\}\cap\{\text{Retrieved items}\}|}{|\{\text{Relevant items}\}|} $$

__Example:__ The reference expected set is `A,B,C,D,E` (5 items), and the retrieved/found set is `B,C,D,F` (4 items). The set of expected items retrieved is `B,C,D` (3 common items). The recall is $$ R = \frac{3}{5} = 60\% $$

In statistics, F-measure (or F1 score) is the harmonic mean of precision $ P $ and recall $ R $

$$ F = \frac{2 (P \times R)}{(P + R)} $$

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Source : https://www.dcode.fr/precision-recall

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