# ~ Precision (classification)

In statistical classification, the precision of a classifier over a class is the ratio between the number of times that class got predicted correctly and the number of times that class got predicted. Namely,

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

where $x$ is the class being predicted.

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