Recall is defined as the proportion of what?

Recall is defined as the proportion of what?

• A. Predicted positives correctly identified
• B. Actual positives correctly identified
• C. Correct classifications over total samples
• D. All positives identified including false positives

Answer: (B)

Recall measures the proportion of actual positive cases that the model correctly identifies as positive. It is defined as true positives divided by the total number of actual positives (TP / (TP + FN)). This is also known as sensitivity, and it highlights how well the model captures all real positives, minimizing false negatives. It differs from precision, which looks at how many of the predicted positives are truly positive (TP / (TP + FP)), and from overall accuracy, which is the proportion of all correct classifications across both positives and negatives. It wouldn’t count all positives including false positives, because that would ignore the crucial distinction between identifying real positives and mislabeling negatives as positives.

Recall measures the proportion of actual positive cases that the model correctly identifies as positive. It is defined as true positives divided by the total number of actual positives (TP / (TP + FN)). This is also known as sensitivity, and it highlights how well the model captures all real positives, minimizing false negatives. It differs from precision, which looks at how many of the predicted positives are truly positive (TP / (TP + FP)), and from overall accuracy, which is the proportion of all correct classifications across both positives and negatives. It wouldn’t count all positives including false positives, because that would ignore the crucial distinction between identifying real positives and mislabeling negatives as positives.