Classification Problems and Predictive Decisions

This lesson introduces classification as the task of making predictive decisions about categories rather than predicting continuous quantities. It frames classifiers as decision systems that use input features to estimate class membership and then convert those estimates into actions.

Students learn the difference between labels, scores, probabilities, and final decisions; why uncertainty matters; and how business or operational costs affect the right classification threshold. The lesson sets up the motivation for logistic regression without yet deriving its mathematics.