What is the purpose of the recall metric in a machine learning model?

Recall is a crucial metric in assessing the performance of a machine learning model, particularly in classification tasks. Its primary purpose is to identify and capture all relevant instances, which refers to the model's ability to find true positives among the total number of actual positives in the dataset.

In scenarios where false negatives are costly or undesirable—such as in medical diagnoses, fraud detection, or safety-critical applications—high recall is especially important. It focuses on ensuring that as many of the actual positive instances as possible are correctly identified by the model. Therefore, recall plays a vital role in understanding how well a model can recognize important outcomes or events.

The other options focus on different aspects of model evaluation: the proportion of positive identifications relates more closely to precision; accuracy assesses the overall correctness of the model across all classifications; and assessing overall efficiency may involve other performance aspects, such as speed or resource consumption, rather than specifically concentrating on the identification of relevant instances. Thus, recall's core mission is accurately represented in the correct choice, which emphasizes the identification of all relevant instances.