A data scientist trains a binary classifier that flags manufacturing defects. The training set contains 4% defective units. The initial model achieves 96% accuracy but misses most defects during validation. Management requires that the model catch as many true defects as possible while keeping false alarms manageable, and they want a single metric that balances both concerns for model selection. Which evaluation metric should the team optimize?