Which metric is most fundamentally concerned with the proportion of actual positives captured by the model?

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Multiple Choice

Which metric is most fundamentally concerned with the proportion of actual positives captured by the model?

Recall is the metric that tells you what fraction of real positive cases your model manages to identify. It’s defined as true positives divided by the sum of true positives and false negatives, so the denominator includes all actual positives. This makes recall a direct measure of how well you’re capturing the positive instances that actually occur, which is critical when missing a positive carries high cost (for example, failing to detect a disease). Other metrics answer different questions. Precision focuses on how many of the things you labeled positive were truly positive, not how many real positives you caught. Accuracy looks at overall correct predictions, which can be misleading if the classes are imbalanced. F1 Score combines precision and recall but does not specifically measure the proportion of actual positives captured on its own.

Recall is the metric that tells you what fraction of real positive cases your model manages to identify. It’s defined as true positives divided by the sum of true positives and false negatives, so the denominator includes all actual positives. This makes recall a direct measure of how well you’re capturing the positive instances that actually occur, which is critical when missing a positive carries high cost (for example, failing to detect a disease).

Other metrics answer different questions. Precision focuses on how many of the things you labeled positive were truly positive, not how many real positives you caught. Accuracy looks at overall correct predictions, which can be misleading if the classes are imbalanced. F1 Score combines precision and recall but does not specifically measure the proportion of actual positives captured on its own.

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