​
Input

  • output (str): The generated text (set of predicted positive items)
  • expectedOutput (str): The reference text (set of ground-truth positive items)

​
Output

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

​
Interpretation

  • Higher scores (closer to 1): A larger fraction of predicted positives are correct (fewer false positives)
  • Lower scores (closer to 0): Many predicted positives are incorrect (more false positives)

​
Formula

Precision=TPTP+FP\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}
This is a Similarity Metric

​
Use Cases

  • Information retrieval and search relevance
  • Classification tasks where avoiding false alarms is critical
  • Evaluating the relevance of generated content