F1 Score
The harmonic mean of precision and recall, balancing false positives and false negatives.
The F1 score is the harmonic mean of precision and recall, combining both into one value that punishes imbalance between them. Because it uses the harmonic mean, a model with perfect precision but zero recall scores 0, not 0.5 — forcing real balance between false positives and false negatives. On the AIF-C01 exam, the key contrast is with accuracy: accuracy looks deceptively high when one class dominates (95% on a fraud set that is 5% fraud), while F1 exposes weak minority-class detection. SageMaker reports F1 among its built-in classification metrics, making it the go-to signal for imbalanced datasets.
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