Classification evaluation is the set of methods used to quantify how well a classifier’s predicted labels match ground-truth labels, typically summarised through metrics derived from a confusion matrix such as precision, recall, and F1 score. It distinguishes between overall accuracy, which can be misleading under class imbalance, and per-class metrics that expose asymmetric error costs. Classification evaluation guides model selection, threshold tuning, and reporting of deployed classifier performance.