Performance Metrics in Machine Learning - Part 2

Model quality is context-dependent, and metric selection should reflect the business cost of false positives, false negatives, and calibration errors.

The article covers common measures such as precision, recall, F1, ROC-AUC, MAE, MSE, and R-squared with practical interpretation notes.

For production ML, evaluation design is a decision framework, not just a reporting checklist.