A company must recalibrate a binary classifier to maximize correct predictions for both positive and negative classes. Which evaluation metric should the ML engineer use for this recalibration?

Accuracy is the most suitable metric when the goal is to maximize correct predictions for both positive and negative classes, as it represents the proportion of total correct predictions (True Positives + True Negatives) out of all predictions. Precision focuses only on the correctness of positive predictions (minimizing false positives), while Recall (Sensitivity) focuses on identifying all actual positive cases (minimizing false negatives). Specificity focuses on identifying all actual negative cases (minimizing false positives). Since the requirement is to maximize correct predictions for both classes, Accuracy provides a balanced view of the model's overall performance across both positive and negative outcomes.