Why Model Evaluation Matters

Model evaluation is the discipline of deciding whether a machine learning model is useful, trustworthy, and worth deploying. In this lesson, Professor Charles Knight introduces evaluation as more than a final accuracy score: it is a structured process for connecting model behavior to business goals, user impact, statistical evidence, and operational risk.

You will learn why evaluation must begin before modeling, why different stakeholders need different performance evidence, and why a model that looks strong on one metric can still fail in production. This foundation prepares you for later lessons on metrics, validation design, uncertainty, subgroup analysis, model comparison, and communication.