Recall

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.

See also Precision, Accuracy and F-score.