Tool to compute statistical measures of Precision and Recall. Precision and recall are two statistical measures which can evaluate sets of items, also called predictive value, specificity and sensitivity.

Precision and Recall - dCode

Tag(s) : Data processing, Mathematics

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Tool to compute statistical measures of Precision and Recall. Precision and recall are two statistical measures which can evaluate sets of items, also called predictive value, specificity and sensitivity.

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

F-measure (or F1 score) is the harmonic mean of precision and recall

$$ F = \frac{2 (\text{precision} \times \text{recall})}{(\text{precision} + \text{recall})} $$

The following diagram includes precision, recall, true positive, false positive, true negative and false negative (source wikipedia)

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

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