Rethinking Model Evaluation: Why Accuracy Isn’t Enough For decades, selecting and deploying machine learning models has largely revolved around a single figure: accuracy. A model reporting 95% accuracy may seem exceptional, implying strong predictive power and reliability. But real-world systems operate in messy, changing environments where data distributions shift, adversaries probe weaknesses, and edge cases…