Classification metrics like accuracy, precision, recall, and F1-score help evaluate model performance. Accuracy shows overall correctness, while precision and recall highlight how well the model handles positive predictions. F1-score balances both. A confusion matrix provides the foundation for these metrics. Choosing the right metric ensures reliable and context-aware classification, especially in imbalanced datasets.