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Input

  • output (str): The generated text (set of items)
  • expectedOutput (str): The reference text (set of items)

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Output

  • Result (float): A score between 0 and 1.

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Interpretation

  • Higher scores (closer to 1): Strong balance of precision and recall
  • Lower scores (closer to 0): Either precision or recall (or both) are weak

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Formula

F1=2⋅Precision⋅RecallPrecision+Recall\mathrm{F1} = \frac{2 \cdot \mathrm{Precision} \cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}} Where: Precision=TPTP+FP,Recall=TPTP+FN\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}},\quad\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}
This is a Similarity Metric

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Use Cases

  • Evaluating classifiers with class imbalance
  • Information extraction and NER
  • Scenarios where both precision and recall matter
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