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Precision is the fraction of predicted positives that are correct and recall the fraction of actual positives found. The F1 score is their harmonic mean, which is far more informative than accuracy when classes are imbalanced.
Precision and recall
P = TP/(TP + FP), R = TP/(TP + FN)
F1 score
F1 = 2PR / (P + R)
Precision is 0.7778, recall 0.7 and F1 0.7368, while accuracy looks flattering at 0.95.
With 95% negatives, always predicting negative scores 95% accuracy while catching nothing — F1 exposes that immediately.