Precision and recall

Definition (classification context)

For (binary) classification tasks, the terms

true positives,

true negatives,

false positives,

and false negatives

compare the results of the classifier under test with trusted external judgments.

Precision and recall are then defined as:^[5]

\text{Precision}=\frac{tp}{tp+fp} \,

\text{Recall}=\frac{tp}{tp+fn} \,

A measure that combines precision and recall is the harmonic mean of precision and recall, the traditional F-measure or balanced F-score:

F = 2 \cdot \frac{\mathrm{precision} \cdot \mathrm{recall}}{ \mathrm{precision} + \mathrm{recall}}