Why 'Good' Machine Learning Models Often Fail: A Guide to Proper Evaluation

Choosing the right evaluation metrics and validation strategy often determines whether a machine learning model succeeds or fails in production. Many engineers optimize for misleading metrics—such as maximizing accuracy in fraud detection or minimizing MSE while harming user experience—leading to models that perform well on test data but fail when deployed. Understanding task-specific metrics, proper train-validation-test splits, cross-validation, and addressing data bias are essential to building trustworthy ML systems.