Choosing Accuracy to Recalibrate a Binary Classifier That Must Maximize Both Positive and Negative Predictions

Why the Answer Is Correct

The requirement is to maximize correct predictions for both positive and negative labels, which means the metric must reward true positives and true negatives together. Accuracy is defined as the total correct predictions, that is true positives plus true negatives, divided by total predictions, so it is exactly the measure that reflects performance on both classes simultaneously. Precision, recall, and specificity each account for only one side of the confusion matrix, so maximizing any one of them can degrade the other, which does not satisfy the stated requirement. The vote was unanimous at 100 for A. Saransundar gave the formula for each metric and showed that only accuracy includes both true positives and true negatives, and aws_Tamilan identified the keyword as maximizing correct predictions of both labels.

Why the Other Options Are Wrong

Precision (B) is computed as true positives over true positives plus false positives, so it measures only how many predicted positives were correct and says nothing about true negatives. Recall (C) is true positives over true positives plus false negatives, so it captures all actual positives while ignoring what happened to the negative class, and maximizing it typically drives false positives up. Specificity (D) is true negatives over true negatives plus false positives, so it captures the negative class alone and ignores the positive class entirely. Each of these three optimizes one side of the confusion matrix at the potential expense of the other, so none can serve as the recalibration target when both classes must be maximized together.

Community Comment Notes

The community was unanimous at 100 for A, and Saransundar's comment was the most rigorous, listing the formula for all four metrics and concluding that only accuracy is (TP + TN) over total predictions. GiorgioGss supplied the accuracy formula and cited the SageMaker Clarify accuracy evaluation documentation. kyo raised a fair technical caveat, noting that for imbalanced data the F1 score is generally preferred over the confusion-matrix-derived metrics and that ideally option A would be F1 score, but acknowledged that given the four choices available, accuracy is the most reasonable answer. The question itself does not mention class imbalance, so the caveat is about ideal practice rather than about this scenario.