When making a binary classification or decision, such as a disease diagnosis, recall is the probability that if the person really has the disease, or in general if the classification really is positive, then we corectly make the decision, for example, if the person has the disease then we do diagnose it. If a decision has a high recall, then the probability of a false negative is low. Often increasing recall reduces precision; this is called the precision–recall trade-off.
Defined on page 180
Used on Chap. 9: pages 180, 181, 182, 193, 196; Chap. 18: page 447