Recall

Properties2

Type	Concept
Note created	Feb 17, 2025

When evaluating a Machine Learning classification model, recall (or sensitivity), on the other hand, is the fraction of relevant instances that were indeed retrieved. In the context of binary classification, it could be understood and the fraction of existing positives that were predicted as such.

$$\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}$$

It is very tightly coupled with another error metric, precision. Real life models usually require by their very nature a higher precision than recall or the other way around.