What does Recall measure in machine learning models?

Recall, also known as sensitivity or true positive rate, is an important metric in evaluating the performance of classification models, particularly when the cost of false negatives is high. Recall measures the ratio of correctly predicted positive observations to the actual positives in the dataset. This means it quantifies how well the model is able to identify all relevant instances (true positives) out of the total actual positives.

For instance, in a medical diagnosis scenario, recall would indicate how many patients with a particular disease were correctly identified by the model. A high recall value signifies that the model successfully identifies most of the actual positives, which is critical in applications where missing a positive instance can lead to severe consequences.

In contrast, the other options measure different aspects of model performance. The total number of correct predictions made by the model does not differentiate between positive and negative predictions and therefore does not provide insights into how well the model identifies positive instances specifically. The proportion of true positives among the total predicted positives defines precision, not recall, focusing instead on the accuracy of positive predictions relative to all predicted positives. Lastly, the accuracy of the model in predicting negative observations refers to a broader measure of overall model performance, which may lead to misleading interpretations if the class distribution is imbalanced. Each of these