Building on the general concepts of evaluation, this chapter concentrates on methods for assessing classification models. You will learn how to quantify performance using standard metrics.

We begin with accuracy, covering its calculation and potential shortcomings, especially in scenarios like imbalanced datasets. The chapter then explains the confusion matrix as a tool for deeper analysis, defining its core components: True Positives ($TP$), False Positives ($FP$), True Negatives ($TN$), and False Negatives ($FN$).

Using these elements, we will examine precision (measuring exactness), recall (measuring completeness), and the F1-score, which combines both. We will also discuss the common trade-off observed between precision and recall. The goal is to provide you with the practical ability to calculate and interpret these essential classification metrics through clear explanations and examples.