Precision and Recall

Precision measures the fraction of predicted positives that are truly positive, while recall measures the fraction of actual positives that are correctly identified. The two metrics trade off: high-precision systems avoid false positives but may miss many true positives, while high-recall systems catch most positives but generate more false alarms. The F1 score combines them as the harmonic mean. Precision and recall are especially important in NLP tasks like named entity recognition and information retrieval where both false positives and false negatives carry distinct costs.