In statistical classification, the *recall* of a classifier
over a class is the ratio between the number of times a class got
predicted correctly and the number of times that class actually appears
in the corresponding dataset.

$\text{recall}(x) = \frac{N_{\text{correctly predicted}}(x)}{N(x)},$

where $x$ is the class being classified.

Typically, this appears in binary classification, where we want to compare the number of true positives against the number of positives.