Comprehensive Model Evaluation: Moving Beyond Simple Accuracy Metrics

In my years developing ML systems for high-stakes applications—from genomics research to real-time betting analytics—I’ve learned that model evaluation is where theory meets reality. A model that shows 95% accuracy in testing can still fail catastrophically in production if we haven’t evaluated it properly. The key is understanding that different problems require different evaluation approaches.

The Accuracy Trap: Why One Metric Isn’t Enough

Accuracy seems intuitive: how often does our model make correct predictions? But this metric can be dangerously misleading. Consider fraud detection where only 0.1% of transactions are fraudulent. A lazy model that always predicts “legitimate” achieves 99.9% accuracy while being completely useless.