Precision measures the proportion of positive predictions that are actually correct, while recall measures the proportion of actual positives that are correctly identified. A fraud detector with high precision rarely flags legitimate transactions; one with high recall catches most fraud cases. They are often in tension: raising one lowers the other. The F1-score is their harmonic mean, providing a single balanced metric. Understanding the precision-recall tradeoff is essential for any classification task with real-world consequences.