f1 score

The F1 score is a human-made evaluation metric used in machine learning and information retrieval to measure the accuracy of a classification model. It is the harmonic mean of precision and recall: F1 = 2 · (precision · recall) / (precision + recall). It provides a single score that balances both false positives (precision) and false negatives (recall), making it especially valuable when class distributions are imbalanced. Unlike simple accuracy, the F1 score penalizes extreme imbalances between precision and recall equally. It ranges from 0 (worst) to 1 (perfect). Used extensively in binary classification, it generalizes to multi-class settings via macro-, micro-, or weighted averaging. [formal: punctum F1 | substrate: behavior | horizon: hours | explicit: yes | epoch: 0.01]