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The F1 score combines precision and recall using which kind of average?

The harmonic mean.

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  1. The harmonic mean ✔
  2. The simple arithmetic mean
  3. The median

Why: F1 is 2 times precision times recall, divided by precision plus recall. The harmonic mean drags the score down if either one is low, so a model cannot hide a bad recall behind great precision.

Huh, didn't know that: If precision and recall are both a perfect 1.0, F1 is also 1.0.

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F1 is the strict parent of metrics. One bad grade and the whole report card drops.
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References

Answer: The harmonic mean. F1 is 2 times precision times recall, divided by precision plus recall. The harmonic mean drags the score down if either one is low, so a model cannot hide a bad recall behind great precision.

  1. Google ML Crash Course: Accuracy, recall, precision developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall
    The F1 score is the harmonic mean (a kind of average) of precision and recall.
    Checked 2026-10-10.

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