Key NLP Evaluation Metrics Explained: Precision, Recall, F1, and Confusion Matrix

Evaluating NLP models goes beyond simple accuracy, which can be misleading when datasets are imbalanced or certain errors carry higher costs. Precision measures how often a model's positive predictions are actually correct, while recall captures how many real positives the model successfully identifies. These two metrics often trade off against each other, making it difficult to optimise both simultaneously. The F1 score addresses this by combining precision and recall into a single balanced measure using their harmonic mean. A confusion matrix complements these metrics by visually breaking down true positives, false positives, true negatives, and false negatives, helping engineers pinpoint exactly where a model is going wrong.