F1 score

One number that combines precision and recall as their harmonic mean, so it is high only when both are high.

F1 is 2 × precision × recall divided by their sum. Because it is a harmonic mean, a system with 95% precision and 10% recall gets an F1 of about 18%, not a comfortable average. It is useful for comparing versions when classes are unbalanced and plain accuracy misleads. It weighs both kinds of error equally; when one costs more, the F-beta variant shifts the weight.

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Related terms

Precision (AI metric): The share of returned results that are actually correct: of everything the system flagged or retrieved, how much was right.
Recall (AI metric): The share of all correct items that the system actually found: of everything it should have caught, how much it did.
Confusion matrix: A table that counts a classifier’s right and wrong decisions by type: true and false positives, true and false negatives.

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